Add Streamlit app and Xet image helpers

.gitattributes

@@ -1,35 +1,3 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
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-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
 *.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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-*.tar filter=lfs diff=lfs merge=lfs -text
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-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+img/* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
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+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+#Pipfile.lock
+
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+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#uv.lock
+
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+#poetry.lock
+
+# pdm
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+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
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+.pypirc

Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+FROM python:3.9-slim
 
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy requirements and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
+# Copy the application code
+COPY . .
 
+# Expose the Streamlit port
 EXPOSE 8501
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Command to run the application
+CMD ["streamlit", "run", "app.py", "--server.address=0.0.0.0", "--server.port=8501"]

LICENSE

@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
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+to attach them to the start of each source file to most effectively
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+    (at your option) any later version.
+
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+
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+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
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+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
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+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

app.py

@@ -0,0 +1,37 @@
+# Main entry point of the Streamlit application
+
+import streamlit as st
+import sys
+from pathlib import Path
+
+APP_DIR = Path(__file__).resolve().parent
+STREAMLIT_SRC = APP_DIR / "src"
+if str(STREAMLIT_SRC) not in sys.path:
+    sys.path.append(str(STREAMLIT_SRC))
+
+from pages import about_page, contact_page, home_page
+
+def main():
+    st.set_page_config(
+        page_title="Chaptly (YouTube Tutor Chatbot)",
+        page_icon="📚",
+        layout="wide",
+        initial_sidebar_state="expanded",
+    )
+    
+    st.sidebar.title("Navigation")
+    
+    # Sidebar navigation
+    pages = {
+        "Home": home_page,
+        "About": about_page,
+        "Contact": contact_page,
+    }
+    
+    selection = st.sidebar.radio("Go to", list(pages.keys()))
+    
+    # Render the selected page
+    pages[selection]()
+
+if __name__ == "__main__":
+    main()

config.toml

@@ -0,0 +1,17 @@
+[general]
+email = "[email protected]"
+# The email address for the Streamlit sharing service.
+
+[server]
+headless = true
+port = 8501
+enableCORS = false
+# Configuration for the server, including running in headless mode and setting the port.
+
+[theme]
+primaryColor = "#F39C12"
+backgroundColor = "#FFFFFF"
+secondaryBackgroundColor = "#F0F0F0"
+textColor = "#000000"
+font = "sans serif"
+# Theme settings for the Streamlit application, including colors and font.

huggingface-metadata.yaml

@@ -0,0 +1,8 @@
+# File: .github/huggingface-metadata.yaml
+title: Chaptly AI
+emoji: 📕
+colorFrom: indigo
+colorTo: blue
+sdk: docker
+app_port: 8501
+pinned: false

requirements.txt

@@ -1,3 +1,13 @@
-altair
-pandas
-streamlit
+streamlit>=1.24.0
+pandas>=1.5.0
+numpy>=1.23.0
+plotly>=5.13.0
+folium>=0.14.0
+streamlit-folium>=0.11.0
+requests>=2.28.0
+python-dotenv>=1.0.0
+ortools>=9.6.0
+geopy
+matplotlib
+boto3>=1.28.0
+altair

scripts/api_lambda_call.py

@@ -0,0 +1,24 @@
+import os, time, hashlib, hmac, requests
+
+API_URL = os.environ["CHAPTLY_API_URL"]
+API_SECRET = os.environ["CHAPTLY_API_SECRET"]
+API_KEY = os.environ["API_KEYS"]  # optional, only if using ApiKeyMiddleware
+
+def signed_request(method: str, path: str, json_payload: dict | None = None):
+    body_bytes = (json.dumps(json_payload or {}) ).encode("utf-8")
+    body_hash = hashlib.sha256(body_bytes).hexdigest()
+    timestamp = str(int(time.time()))
+    canonical = "\n".join([method.upper(), path, body_hash, timestamp])
+    signature = hmac.new(API_SECRET.encode(), canonical.encode(), hashlib.sha256).hexdigest()
+
+    headers = {
+        "X-Timestamp": timestamp,
+        "X-Signature": signature,
+    }
+    if API_KEY:
+        headers["X-API-Key"] = API_KEY
+
+    url = f"{API_URL}{path}"
+    resp = requests.request(method, url, json=json_payload, headers=headers)
+    resp.raise_for_status()
+    return resp.json()

scripts/create_iam_role.py

@@ -0,0 +1,144 @@
+"""Utility script to provision S3 read-only credentials for the Streamlit app.
+
+This helper creates (or reuses) an IAM user and optional role that have
+`s3:ListBucket` / `s3:GetObject` access to the `chaptly-rag` bucket. It then
+prints the access key and secret so they can be set as environment variables for the Streamlit app.
+
+Usage:
+
+```bash
+python streamlit/scripts/create_iam_role.py \
+  --entity-name streamlit-cache-reader \
+  --bucket chaptly-rag
+```
+
+The AWS account credentials used to run this script must already have IAM
+permissions to create users/roles, attach policies, and create access keys.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from typing import Any, Dict, Optional
+
+import boto3
+from botocore.exceptions import ClientError
+
+S3_POLICY_NAME = "StreamlitS3ReadOnly"
+DEFAULT_ENTITY_NAME = "streamlit-cache-reader"
+DEFAULT_BUCKET = "chaptly-rag"
+
+
+def _build_policy(bucket: str) -> Dict[str, Any]:
+	resource_arn = f"arn:aws:s3:::{bucket}"
+	object_arn = f"{resource_arn}/*"
+	return {
+		"Version": "2012-10-17",
+		"Statement": [
+			{
+				"Effect": "Allow",
+				"Action": ["s3:ListBucket"],
+				"Resource": resource_arn,
+			},
+			{
+				"Effect": "Allow",
+				"Action": ["s3:GetObject"],
+				"Resource": object_arn,
+			},
+		],
+	}
+
+
+def ensure_user(iam, user_name: str) -> None:
+	try:
+		iam.get_user(UserName=user_name)
+		print(f"Reusing IAM user '{user_name}'.")
+	except ClientError as exc:
+		if exc.response["Error"]["Code"] == "NoSuchEntity":
+			iam.create_user(UserName=user_name)
+			print(f"Created IAM user '{user_name}'.")
+		else:
+			raise
+
+
+def attach_inline_policy(iam, user_name: str, bucket: str) -> None:
+	policy_doc = json.dumps(_build_policy(bucket))
+	iam.put_user_policy(UserName=user_name, PolicyName=S3_POLICY_NAME, PolicyDocument=policy_doc)
+	print(f"Attached inline S3 read policy '{S3_POLICY_NAME}' to user '{user_name}'.")
+
+
+def create_access_key(iam, user_name: str) -> Dict[str, str]:
+	response = iam.create_access_key(UserName=user_name)
+	access_key = response["AccessKey"]
+	return {
+		"AWS_ACCESS_KEY_ID": access_key["AccessKeyId"],
+		"AWS_SECRET_ACCESS_KEY": access_key["SecretAccessKey"],
+	}
+
+
+def ensure_role(iam, role_name: str, bucket: str, assume_policy: Dict[str, Any]) -> None:
+	try:
+		iam.get_role(RoleName=role_name)
+		print(f"Reusing IAM role '{role_name}'.")
+	except ClientError as exc:
+		if exc.response["Error"]["Code"] == "NoSuchEntity":
+			iam.create_role(RoleName=role_name, AssumeRolePolicyDocument=json.dumps(assume_policy))
+			print(f"Created IAM role '{role_name}'.")
+		else:
+			raise
+	policy_doc = json.dumps(_build_policy(bucket))
+	iam.put_role_policy(RoleName=role_name, PolicyName=S3_POLICY_NAME, PolicyDocument=policy_doc)
+	print(f"Attached inline S3 read policy '{S3_POLICY_NAME}' to role '{role_name}'.")
+
+
+def parse_args() -> argparse.Namespace:
+	parser = argparse.ArgumentParser(description="Provision S3 read-only IAM credentials for Streamlit.")
+	parser.add_argument("--entity-name", default=DEFAULT_ENTITY_NAME, help="IAM user/role name to create or reuse.")
+	parser.add_argument("--bucket", default=DEFAULT_BUCKET, help="S3 bucket name (default: chaptly-rag).")
+	parser.add_argument(
+		"--create-role",
+		action="store_true",
+		help="Also create an IAM role with the same name and inline policy (optional).",
+	)
+	parser.add_argument(
+		"--role-trust",
+		default=None,
+		help="Path to a JSON file describing the assume-role trust policy (required if --create-role).",
+	)
+	return parser.parse_args()
+
+
+def main() -> None:
+	args = parse_args()
+	iam = boto3.client("iam")
+	ensure_user(iam, args.entity_name)
+	attach_inline_policy(iam, args.entity_name, args.bucket)
+	credentials = create_access_key(iam, args.entity_name)
+	print("\nAdd the following values to your Streamlit secrets:")
+	for key, value in credentials.items():
+		print(f"{key} = {value}")
+	print("AWS_REGION = ap-southeast-1")
+	print(f"CHAPTLY_S3_BUCKET = {args.bucket}")
+
+	if args.create_role:
+		if not args.role_trust:
+			raise SystemExit("--role-trust JSON file is required when --create-role is set.")
+		with open(args.role_trust, "r", encoding="utf-8") as handle:
+			assume_policy = json.load(handle)
+		ensure_role(iam, args.entity_name, args.bucket, assume_policy)
+		print(
+			"\nIAM role created. Attach this role to an EC2/Lambda/Streamlit Cloud runner and expose temporary credentials via environment variables."
+		)
+
+
+if __name__ == "__main__":
+	try:
+		main()
+	except ClientError as exc:
+		print(f"AWS error: {exc}")
+		sys.exit(1)
+	except KeyboardInterrupt:
+		print("Aborted by user.")
+		sys.exit(130)

src/pages/__init__.py

@@ -0,0 +1,7 @@
+"""Streamlit page entrypoints."""
+
+from ._home_page import home_page
+from ._about_page import about_page
+from ._contact_page import contact_page
+
+__all__ = ["home_page", "about_page", "contact_page"]

src/pages/_about_page.py

@@ -0,0 +1,168 @@
+import streamlit as st
+
+from utils.helpers import get_image_base64, get_remote_image_url, resolve_image_source
+
+
+def about_page() -> None:
+    st.title("About Chaptly AI")
+    st.markdown(
+        "Chaptly is a conversational tutor that turns long-form YouTube lectures into searchable,"
+        " grounded study sessions using Gemini, FastAPI, and a fully serverless AWS backbone."
+    )
+
+    cover_src = resolve_image_source("cover_page.jpg")
+    if cover_src:
+        st.image(cover_src, caption="Serverless AI data pipeline overview", width=512)
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Project Overview")
+    st.write(
+        "Chaptly indexes pre-approved educational channels, downloads transcripts, chunks and embeds them,"\
+        " then exposes bookmarks, semantic search, QA, quizzes, and summaries via a single FastAPI Lambda."\
+        " Downstream clients (Hugging Face Space, Streamlit, CLI) interact through API Gateway using API keys"\
+        " and strict channel whitelists."
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    with st.expander("Whitelisted YouTube Channels ✅", expanded=True):
+        # Channel data: (logo filename, url, display name)
+        channel_data = [
+            ("MIT OpenCourseWare.png", "https://www.youtube.com/@mitocw", "MIT OpenCourseWare"),
+            ("Stanford Online.png", "https://www.youtube.com/@stanfordonline", "Stanford Online"),
+            ("Harvard.png", "https://www.youtube.com/@harvard", "Harvard"),
+            ("freeCodeCamp.png", "https://www.youtube.com/@freecodecamp", "freeCodeCamp.org"),
+            ("TEDx.png", "https://www.youtube.com/@TEDx", "TEDx Talks"),
+            ("SciShow.png", "https://www.youtube.com/@SciShow", "SciShow"),
+            ("Udacity.png", "https://www.youtube.com/@Udacity", "Udacity"),
+            ("ProgrammingWithMosh.png", "https://www.youtube.com/@programmingwithmosh", "Programming with Mosh"),
+            ("Computerphile.png", "https://www.youtube.com/@Computerphile", "Computerphile"),
+            ("edX.png", "https://www.youtube.com/@edXOnline", "edX"),
+            ("TED.png", "https://www.youtube.com/@TED", "TED"),
+            ("YaleCourses.png", "https://www.youtube.com/@YaleCourses", "YaleCourses"),
+            ("Veritasium.png", "https://www.youtube.com/@veritasium", "Veritasium"),
+            ("3blue1brown.png", "https://www.youtube.com/@3blue1brown", "3blue1brown"),
+            ("CrashCourse.png", "https://www.youtube.com/@crashcourse", "CrashCourse"),
+            ("KhanAcademy.png", "https://www.youtube.com/@khanacademy", "Khan Academy"),
+            ("minutephysics.png", "https://www.youtube.com/@minutephysics", "minutephysics"),
+            ("numberphile.png", "https://www.youtube.com/@numberphile", "numberphile"),
+            ("TEDEd.png", "https://www.youtube.com/@TEDEd", "TED-Ed"),
+            ("coursera.png", "https://www.youtube.com/@coursera", "Coursera"),
+        ]
+        # Display in rows of 4 columns
+        num_cols = 4
+        for row_start in range(0, len(channel_data), num_cols):
+            cols = st.columns(num_cols)
+            for col_idx in range(num_cols):
+                idx = row_start + col_idx
+                if idx >= len(channel_data):
+                    continue
+                img_filename, url, alt = channel_data[idx]
+                with cols[col_idx]:
+                    img_base64 = get_image_base64(img_filename)
+                    img_src = (
+                        f"data:image/png;base64,{img_base64}"
+                        if img_base64
+                        else get_remote_image_url(img_filename)
+                    )
+                    st.markdown(
+                        f"""
+                        <a href='{url}' target='_blank'>
+                            <img src='{img_src}' style='display:block;margin:auto;width:48px;height:48px;object-fit:contain;' alt='{alt}'/>
+                        </a>
+                        """,
+                        unsafe_allow_html=True
+                    )
+                    st.markdown(f"<div style='text-align:center;'><a href='{url}' target='_blank'>{alt}</a></div>", unsafe_allow_html=True)
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.info(
+        "Only whitelisted channels are supported due to copyright restrictions and to ensure all ingested content is suitable for educational and tutoring purposes. This policy protects creators' intellectual property and guarantees high-quality, reliable study material for learners."
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Key Features")
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown(
+            "- **One-click ingestion** queues a background job that fetches transcripts and stores artifacts in S3.\n"
+            "- **Grounded QA** cites timestamped transcript snippets for every answer.\n"
+            "- **Bookmarks & summaries** give learners instant structure for long lectures."
+        )
+    with col2:
+        st.markdown(
+            "- **Quiz builder** mixes MCQ/open prompts sourced from Gemini.\n"
+            "- **Semantic search** surfaces the best matching transcript spans instantly.\n"
+            "- **Strict channel policy** enforces ingestion only from approved education partners."
+        )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("How Chaptly AI Transforms Your Study Experience")
+    st.markdown("Chaptly AI is designed to help learners master complex material quickly and at scale:")
+    feature_emojis = [
+        "🚀", "🔎", "📑", "📝", "💡", "✅"
+    ]
+    feature_titles = [
+        "One-click Ingestion",
+        "Grounded QA",
+        "Bookmarks & Summaries",
+        "Quiz Builder",
+        "Semantic Search",
+        "Strict Channel Policy"
+    ]
+    feature_descriptions = [
+        "Instant access to hours of lectures—no manual steps.",
+        "Reliable, context-rich answers—less searching, more learning.",
+        "Digest lectures into key sections for easy review.",
+        "Self-test and reinforce knowledge instantly.",
+        "Find the exact explanation you need, fast.",
+        "Only high-quality, trusted educational content."
+    ]
+    cols = st.columns(3)
+    for i in range(3):
+        with cols[i]:
+            st.markdown(f"{feature_emojis[i]} **{feature_titles[i]}**")
+            st.write(feature_descriptions[i])
+    cols2 = st.columns(3)
+    for i in range(3, 6):
+        with cols2[i-3]:
+            st.markdown(f"{feature_emojis[i]} **{feature_titles[i]}**")
+            st.write(feature_descriptions[i])
+    st.markdown(
+        "<br><b>Together, these features empower users to learn faster, retain more, and scale their study efforts across multiple subjects and lectures—making Chaptly AI a powerful tool for modern, rapid, and effective education.</b>", unsafe_allow_html=True
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Tools & Technologies")
+    st.markdown(
+        "| Layer | Stack |\n| --- | --- |\n"
+        "| Backend | FastAPI + Pydantic running on AWS Lambda via Mangum |\n"
+        "| Storage | AWS S3 (artifacts + Lambda ZIP), DynamoDB (`ChaptlyJobs`), Secrets Manager |\n"
+        "| AI | Google Gemini (text-embedding-004, 1.5 Flash) |\n"
+        "| Infra | Terraform (S3, DynamoDB, IAM, Lambda, API Gateway, CloudWatch) |\n"
+        "| Frontend | Streamlit & Hugging Face Space clients |"
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Architecture & Flow")
+    arch_src = resolve_image_source("user_flow_system_architecture.jpg")
+    if arch_src:
+        st.image(arch_src, caption="User Flow & System Architecture", width=800)
+    st.write(
+        "1. User submits a YouTube URL from an approved channel.\n"
+        "2. API Gateway forwards the request to the Lambda-hosted FastAPI app.\n"
+        "3. Background worker fetches transcripts, chunks + embeds with Gemini, and stores artifacts in S3 with lifecycle policies.\n"
+        "4. Job metadata lives in DynamoDB and is polled via `/videos/process/{job_id}`.\n"
+        "5. Subsequent queries (bookmarks, QA, summary, quiz, search) reuse cached artifacts for sub-second responses.\n"
+        "6. Logs & metrics stream into CloudWatch while Terraform keeps every resource in sync."
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Getting Started")
+    st.markdown(
+        "1. Use the **Home** page to upload a YouTube lecture, browse bookmarks, and chat with Gemini.\n"
+        "2. Visit **Contact** page for support, licensing, and GitHub links."
+    )
+
+
+if __name__ == "__main__":
+    about_page()

src/pages/_contact_page.py

@@ -0,0 +1,39 @@
+import streamlit as st
+
+
+def contact_page() -> None:
+    st.title("Contact & Support")
+    st.markdown(
+        "Have a question about Chaptly AI, need help onboarding the API, or want to contribute? "
+        "Reach out through the channels below."
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Primary Contact")
+    st.markdown("- **Email**: [[email protected]](mailto:[email protected])")
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("GitHub & Issues")
+    st.markdown(
+        "- Source: [Github Repository](https://github.com/Jhwong19/chaptly-ai-public)\n"
+        "- Please open GitHub Issues for bug reports, feature requests, or deployment questions."
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("License")
+    st.markdown(
+        "Chaptly AI ships under the MIT License. You are free to fork, adapt, and deploy, provided the license"
+        " notice remains intact. See the repository `LICENSE` file for full terms."
+    )
+
+    st.markdown("<hr>", unsafe_allow_html=True)
+    st.header("Security & Responsible Use")
+    st.markdown(
+        "- Keep API keys and proxy credentials out of public repos.\n"
+        "- Only ingest content from the approved educational channel list.\n"
+        "- Contact us immediately if you suspect a credential leak or data exposure."
+    )
+
+
+if __name__ == "__main__":
+    contact_page()

src/pages/_home_page.py

@@ -0,0 +1,628 @@
+from __future__ import annotations
+
+from typing import Any, Dict, List, Tuple
+
+import requests
+import streamlit as st
+
+from utils.helpers import (
+    ask_question,
+    fetch_bookmarks,
+    fetch_job_status,
+    fetch_quiz,
+    fetch_summary,
+    get_image_base64,
+    get_remote_image_url,
+    list_cached_videos,
+    ping_health,
+    resolve_image_source,
+    semantic_search,
+    seconds_to_timestamp,
+    start_ingestion,
+    youtube_timestamp_link,
+)
+from utils.settings import APPROVED_CHANNELS
+
+BOOKMARK_SUMMARY_WORDS = 100
+
+
+def _init_session_state() -> None:
+    defaults = {
+        "step_done": 0,
+        "auto_open": {1: True, 2: False, 3: False, 4: False, 5: False},
+        "selected_video_id": "",
+        "selected_video_title": "",
+        "selected_channel_name": "",
+        "job_id_by_video": {},
+        "recent_job_status": None,
+        "cached_videos": [],
+        "bookmarks_by_video": {},
+        "bookmark_summaries_by_video": {},
+        "summary_by_video": {},
+        "quiz_by_video": {},
+        "search_results_by_video": {},
+        "qa_history_by_video": {},
+    }
+    for key, value in defaults.items():
+        st.session_state.setdefault(key, value)
+    st.session_state.setdefault("chat_history", [])
+    st.session_state.setdefault("chat_video_id", "")
+    if not st.session_state.get("cached_videos"):
+        st.session_state["cached_videos"] = list_cached_videos()
+
+
+def _is_step_open(step: int) -> bool:
+    return bool(st.session_state.get("auto_open", {}).get(step, False))
+
+
+def _advance_to_step(step: int) -> None:
+    st.session_state["step_done"] = max(st.session_state["step_done"], step)
+    if step < 5:
+        st.session_state["auto_open"][step] = False
+        st.session_state["auto_open"][step + 1] = True
+        _set_expander_override(_step_expander_key(step + 1), True)
+
+
+def _expander_overrides() -> Dict[str, bool]:
+    return st.session_state.setdefault("_expander_overrides", {})
+
+
+def _set_expander_override(key: str, expanded: bool) -> None:
+    _expander_overrides()[key] = expanded
+    st.session_state.pop(f"_expander_initialized_{key}", None)
+
+
+def _expander_container(key: str, label: str, default_open: bool):
+    overrides = _expander_overrides()
+    init_key = f"_expander_initialized_{key}"
+    expanded_arg = None
+    if key in overrides:
+        expanded_arg = overrides.pop(key)
+    elif not st.session_state.get(init_key):
+        expanded_arg = default_open
+    if expanded_arg is None:
+        container = st.expander(label)
+    else:
+        container = st.expander(label, expanded=expanded_arg)
+    st.session_state[init_key] = True
+    return container
+
+
+def _step_expander_key(step: int) -> str:
+    return f"step_{step}"
+
+
+def _step_expander(step: int, label: str):
+    return _expander_container(_step_expander_key(step), label, _is_step_open(step))
+
+
+def _named_expander(key: str, label: str, default_open: bool = False):
+    return _expander_container(key, label, default_open)
+
+
+def _refresh_cached_videos_list() -> None:
+    if hasattr(list_cached_videos, "clear"):
+        list_cached_videos.clear()
+    st.session_state["cached_videos"] = list_cached_videos()
+
+
+def _render_sidebar_stepper() -> None:
+    with st.sidebar:
+        st.markdown("### Progress")
+        sidebar_labels = [
+            "Insert YouTube Lecture URL",
+            "Browse bookmarks",
+            "Summary & Quizzes",
+            "Q&A",
+            "Smart Search (Optional)",
+        ]
+        for step, label in enumerate(sidebar_labels, start=1):
+            status = "✅" if st.session_state["step_done"] >= step else "⬜️"
+            st.write(f"{status} Step {step}: {label}")
+
+
+def _render_api_status() -> None:
+    try:
+        health = ping_health()
+        if health.get("status") == "ok":
+            st.caption("✅ API online")
+        else:
+            st.caption("❌ API offline")
+    except RuntimeError as exc:
+        st.caption(f"❌ {str(exc)}")
+
+
+def _fetch_youtube_metadata(video_url: str) -> Dict[str, Any] | None:
+    if not video_url:
+        return None
+    oembed_url = f"https://www.youtube.com/oembed?url={video_url}&format=json"
+    try:
+        response = requests.get(oembed_url, timeout=10)
+        if response.status_code == 200:
+            return response.json()
+    except requests.RequestException:
+        return None
+    return None
+
+
+def _render_whitelist_expander() -> None:
+    channel_data = [
+        ("MIT OpenCourseWare.png", "https://www.youtube.com/@mitocw", "MIT OpenCourseWare"),
+        ("Stanford Online.png", "https://www.youtube.com/@stanfordonline", "Stanford Online"),
+        ("Harvard.png", "https://www.youtube.com/@harvard", "Harvard"),
+        ("freeCodeCamp.png", "https://www.youtube.com/@freecodecamp", "freeCodeCamp.org"),
+        ("TEDx.png", "https://www.youtube.com/@TEDx", "TEDx Talks"),
+        ("SciShow.png", "https://www.youtube.com/@SciShow", "SciShow"),
+        ("Udacity.png", "https://www.youtube.com/@Udacity", "Udacity"),
+        ("ProgrammingWithMosh.png", "https://www.youtube.com/@programmingwithmosh", "Programming with Mosh"),
+        ("Computerphile.png", "https://www.youtube.com/@Computerphile", "Computerphile"),
+        ("edX.png", "https://www.youtube.com/@edXOnline", "edX"),
+        ("TED.png", "https://www.youtube.com/@TED", "TED"),
+        ("YaleCourses.png", "https://www.youtube.com/@YaleCourses", "YaleCourses"),
+        ("Veritasium.png", "https://www.youtube.com/@veritasium", "Veritasium"),
+        ("3blue1brown.png", "https://www.youtube.com/@3blue1brown", "3blue1brown"),
+        ("CrashCourse.png", "https://www.youtube.com/@crashcourse", "CrashCourse"),
+        ("KhanAcademy.png", "https://www.youtube.com/@khanacademy", "Khan Academy"),
+        ("minutephysics.png", "https://www.youtube.com/@minutephysics", "minutephysics"),
+        ("numberphile.png", "https://www.youtube.com/@numberphile", "numberphile"),
+        ("TEDEd.png", "https://www.youtube.com/@TEDEd", "TED-Ed"),
+        ("coursera.png", "https://www.youtube.com/@coursera", "Coursera"),
+    ]
+    num_cols = 4
+    with st.expander("Whitelisted YouTube Channels ✅", expanded=False):
+        for row_start in range(0, len(channel_data), num_cols):
+            cols = st.columns(num_cols)
+            for col_idx in range(num_cols):
+                idx = row_start + col_idx
+                if idx >= len(channel_data):
+                    continue
+                img_filename, url, alt = channel_data[idx]
+                img_base64 = get_image_base64(img_filename)
+                img_src = (
+                    f"data:image/png;base64,{img_base64}"
+                    if img_base64
+                    else get_remote_image_url(img_filename)
+                )
+                with cols[col_idx]:
+                    st.markdown(
+                        f"""
+                        <a href='{url}' target='_blank'>
+                            <img src='{img_src}' style='display:block;margin:auto;width:48px;height:48px;object-fit:contain;' alt='{alt}'/>
+                        </a>
+                        """,
+                        unsafe_allow_html=True,
+                    )
+                    st.markdown(
+                        f"<div style='text-align:center;'><a href='{url}' target='_blank'>{alt}</a></div>",
+                        unsafe_allow_html=True,
+                    )
+
+
+def _render_step_one() -> None:
+    with _step_expander(1, "Step 1 — Insert YouTube Lecture URL"):
+        video_url = st.text_input(
+            "YouTube URL",
+            value=st.session_state.get("step1_video_url", ""),
+            placeholder="https://www.youtube.com/watch?v=GOgA8JGUiwI",
+            key="step1_video_url_input",
+        )
+        metadata = st.session_state.get("step1_metadata")
+        if video_url and video_url != st.session_state.get("step1_metadata_url"):
+            metadata = _fetch_youtube_metadata(video_url)
+            st.session_state["step1_metadata"] = metadata
+            st.session_state["step1_metadata_url"] = video_url
+        if metadata:
+            st.markdown(f"<span style='font-size:1.3rem;color:#F63366;font-weight:600;'>{metadata.get('title', 'Untitled lecture')}</span>", unsafe_allow_html=True)
+            st.markdown(f"<span style='font-size:1.3rem;color:#666;font-weight:500;'>Channel: {metadata.get('author_name', 'Unknown')}</span>", unsafe_allow_html=True)
+            st.video(video_url)
+        elif video_url:
+            st.warning("Could not fetch metadata for this URL.")
+        run_col, refresh_col = st.columns([3, 1])
+        with run_col:
+            if st.button("Run", type="primary", disabled=not bool(video_url), key="run_btn_step1"):
+                with st.spinner("Running ingestion..."):
+                    _handle_run(video_url, metadata) if video_url else None
+        with refresh_col:
+            st.button(
+                "Refresh Job Status",
+                disabled=not bool(video_url),
+                on_click=_handle_refresh_job_status if video_url else None,
+                key="refresh_job_status_btn"
+            )
+        if st.session_state.get("recent_job_status"):
+            status = st.session_state["recent_job_status"]
+            state_raw = status.get("state", "unknown") or "unknown"
+            state = state_raw.lower()
+            message = status.get("message", "")
+            job_id = status.get("job_id")
+            show_processing_hint = False
+            processing_states = {"queued", "pending", "running", "processing", "started", "submitted"}
+            if state == "failed" and "not in the approved list" in message:
+                st.error(f"Job {job_id} — failed: YouTube Channel not in the approved list.")
+            elif state == "failed":
+                st.error(f"Job {job_id} — failed: {message}")
+            else:
+                st.info(f"Job {job_id} — {state_raw}: {message}")
+                if state in processing_states:
+                    show_processing_hint = True
+            if show_processing_hint:
+                st.info("Processing may take some time to ingest the YouTube video, transcribe it, and convert it into embeddings.")
+        # Whitelisted Youtube Channels expander closed by default
+        _render_whitelist_expander()
+        with st.expander("What does Run and Job Refresh Status do?", expanded=False):
+            st.markdown(
+                "- Checks whether the lecture artifacts already exist in S3\n"
+                "- Queues ingestion via the FastAPI backend if missing\n"
+                "- `Refresh job status` polls `/videos/process/{job_id}` until the artifacts are ready."
+            )
+        _render_cached_videos()
+
+
+def _handle_run(video_url: str, metadata: Dict[str, Any] | None) -> None:
+    if not video_url or not metadata:
+        st.warning("Provide a valid YouTube URL and ensure metadata loads before running.")
+        return
+    try:
+        response = start_ingestion(video_url)
+    except RuntimeError as exc:
+        st.error(str(exc))
+        _render_whitelist_hint()
+        return
+    video_id = response.get("video_id")
+    job_id = response.get("job_id")
+    if video_id and job_id:
+        st.session_state["job_id_by_video"][video_id] = job_id
+        st.session_state["selected_video_id"] = video_id
+        st.session_state["selected_video_title"] = metadata.get("title", video_id)
+        st.session_state["selected_channel_name"] = metadata.get("author_name", "")
+        # Instead of showing ingestion queued, check job status first
+        try:
+            status = fetch_job_status(job_id)
+            st.session_state["recent_job_status"] = status
+        except RuntimeError as exc:
+            st.session_state["recent_job_status"] = {"job_id": job_id, "state": "failed", "message": str(exc)}
+        _advance_to_step(1)
+    else:
+        st.warning("Backend did not return a job_id/video_id. Check logs.")
+
+
+def _handle_refresh_job_status() -> None:
+    video_id = st.session_state.get("selected_video_id")
+    if not video_id:
+        st.info("Select or ingest a video first.")
+        return
+    job_id = st.session_state["job_id_by_video"].get(video_id)
+    if not job_id:
+        st.warning("No job associated with this video yet.")
+        return
+    try:
+        status = fetch_job_status(job_id)
+        st.session_state["recent_job_status"] = status
+        _refresh_cached_videos_list()
+        # Do NOT advance step or change auto_open here, so expander state is preserved
+        st.info(f"State: {status.get('state')} — {status.get('message', '')}")
+    except RuntimeError as exc:
+        st.error(str(exc))
+
+
+def _render_whitelist_hint() -> None:
+    st.warning("Channel not currently approved. Approved channels include:")
+    st.write(", ".join(APPROVED_CHANNELS))
+
+
+def _render_cached_videos() -> None:
+    with _named_expander("cached_videos", "Cached Videos", default_open=True):
+        col_refresh, _ = st.columns([1, 3])
+        if col_refresh.button("Refresh list", key="refresh_cache_list"):
+            _refresh_cached_videos_list()
+        cached = st.session_state.get("cached_videos") or []
+        if not cached:
+            st.info("No cached videos detected yet.")
+            return
+        # Make scrollable if more than 4 videos
+        max_height = "400px" if len(cached) > 4 else "auto"
+        st.markdown(f"<div style='max-height:{max_height};overflow-y:auto;'>", unsafe_allow_html=True)
+        for video in cached:
+            video_id = video.get("video_id")
+            video_title = video.get("title")
+            channel_name = video.get("channel_name")
+            thumbnail_url = video.get("thumbnail_url")
+            video_url = video.get("url") or f"https://www.youtube.com/watch?v={video_id}"
+            # If title or channel_name missing, fetch metadata
+            if not video_title or not channel_name:
+                metadata = _fetch_youtube_metadata(video_url)
+                if metadata:
+                    video_title = metadata.get("title", video_id)
+                    channel_name = metadata.get("author_name", "")
+            else:
+                video_title = video_title or video_id
+                channel_name = channel_name or ""
+            cols = st.columns([1, 3, 1])
+            # Render thumbnail, fallback to YouTube default if missing
+            if thumbnail_url:
+                cols[0].image(thumbnail_url, width='stretch')
+            else:
+                yt_thumb = f"https://img.youtube.com/vi/{video_id}/hqdefault.jpg"
+                cols[0].image(yt_thumb, width='stretch')
+            # Show video name and channel name
+            cols[1].markdown(f"<span style='font-size:1.1rem;font-weight:600;color:#F63366;'>Video: {video_title}</span>", unsafe_allow_html=True)
+            cols[1].markdown(f"<span style='font-size:1.1rem;font-weight:500;color:#666;'>Channel: {channel_name}</span>", unsafe_allow_html=True)
+            cols[1].markdown(f"[Watch on YouTube ↗]({video_url})")
+            if cols[2].button("Use this video", key=f"use_{video_id}"):
+                st.session_state["selected_video_id"] = video_id
+                st.session_state["selected_video_title"] = video_title
+                st.session_state["selected_channel_name"] = channel_name
+                _advance_to_step(1)
+        st.markdown("</div>", unsafe_allow_html=True)
+
+
+def _select_video(label: str, key: str) -> str:
+    cached = st.session_state.get("cached_videos") or []
+    options: List[Tuple[str, str]] = []
+    selected_id = st.session_state.get("selected_video_id", "")
+    selected_label = st.session_state.get("selected_video_title")
+    selected_channel = st.session_state.get("selected_channel_name")
+    # If selected_label or selected_channel missing, fetch metadata
+    if selected_id:
+        if not selected_label or not selected_channel:
+            # Find video_url from cached videos
+            cached = st.session_state.get("cached_videos") or []
+            video_url = None
+            for video in cached:
+                if video.get("video_id") == selected_id:
+                    video_url = video.get("url") or f"https://www.youtube.com/watch?v={selected_id}"
+                    break
+            if video_url:
+                metadata = _fetch_youtube_metadata(video_url)
+                if metadata:
+                    selected_label = metadata.get("title", selected_id)
+                    selected_channel = metadata.get("author_name", "")
+                    st.session_state["selected_video_title"] = selected_label
+                    st.session_state["selected_channel_name"] = selected_channel
+        else:
+            selected_label = selected_label or selected_id
+            selected_channel = selected_channel or ""
+        options.append((selected_id, f"{selected_label} ({selected_channel})"))
+    for video in cached:
+        video_id = video.get("video_id")
+        video_title = video.get("title")
+        channel_name = video.get("channel_name")
+        video_url = video.get("url") or f"https://www.youtube.com/watch?v={video_id}"
+        # If title or channel_name missing, fetch metadata
+        if not video_title or not channel_name:
+            metadata = _fetch_youtube_metadata(video_url)
+            if metadata:
+                video_title = metadata.get("title", video_id)
+                channel_name = metadata.get("author_name", "")
+        else:
+            video_title = video_title or video_id
+            channel_name = channel_name or ""
+        if video_id and video_id != selected_id:
+            options.append((video_id, f"{video_title} ({channel_name})"))
+    if not options:
+        options.append(("", "No cached videos yet"))
+    # Always use 'Video Title' as the field name
+    choice = st.selectbox("Video Title", options, key=key, format_func=lambda item: item[1])
+    video_id = choice[0] if isinstance(choice, tuple) else choice
+    if video_id:
+        st.session_state["selected_video_id"] = video_id
+        # Update title and channel name for selected video
+        for video in cached:
+            if video.get("video_id") == video_id:
+                st.session_state["selected_video_title"] = video.get("title", video_id)
+                st.session_state["selected_channel_name"] = video.get("channel_name", "")
+                break
+    return video_id
+
+
+def _render_step_two() -> None:
+    with _step_expander(2, "Step 2 — Browse bookmarks"):
+        video_id = _select_video("Video", key="bookmarks_video_select")
+        cols = st.columns(2)
+        min_sections = cols[0].slider("Min sections", 1, 10, 3)
+        max_sections = cols[1].slider("Max sections", 3, 15, 8)
+        if st.button("Browse bookmarks", key="browse_bookmarks_btn"):
+            with st.spinner("Fetching bookmarks..."):
+                if not video_id:
+                    st.warning("Select a video first.")
+                    return
+                try:
+                    bookmarks = list(fetch_bookmarks(video_id, max_sections=max_sections, min_sections=min_sections))
+                    st.session_state["bookmarks_by_video"][video_id] = bookmarks
+                    st.session_state["bookmark_summaries_by_video"][video_id] = _summarize_bookmarks(video_id, bookmarks)
+                    _advance_to_step(2)
+                except RuntimeError as exc:
+                    st.error(str(exc))
+
+        _bookmarks = st.session_state["bookmarks_by_video"].get(video_id) or []
+        _summaries = st.session_state["bookmark_summaries_by_video"].get(video_id) or []
+        if _bookmarks:
+            st.markdown("#### Bookmarks")
+            for idx, bookmark in enumerate(_bookmarks):
+                timestamp = int(bookmark.get("timestamp_seconds") or 0)
+                ts_str = bookmark.get("timestamp") or seconds_to_timestamp(timestamp)
+                link = youtube_timestamp_link(video_id, timestamp)
+                title = bookmark.get("title", f"Section {idx + 1}")
+                st.markdown(f"**{idx + 1}. {title}** — `{ts_str}` [Watch ↗]({link})")
+                summary = _summaries[idx]["summary"] if idx < len(_summaries) else bookmark.get("description", "")
+                st.write(summary)
+                sources = _summaries[idx].get("sources", []) if idx < len(_summaries) else []
+                if sources:
+                    with st.expander("Sources", expanded=False):
+                        for source in sources:
+                            ts = int(source.get("timestamp_seconds") or 0)
+                            ts_str_source = source.get("timestamp") or seconds_to_timestamp(ts)
+                            link_source = youtube_timestamp_link(source.get("video_id", video_id), ts)
+                            st.write(f"[{ts_str_source}] {source.get('text', '')}")
+                            st.markdown(f"[Open clip ↗]({link_source})")
+                st.divider()
+
+
+def _summarize_bookmarks(video_id: str, bookmarks: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    results: List[Dict[str, Any]] = []
+    for idx, bookmark in enumerate(bookmarks):
+        bookmark_title = bookmark.get("title") or f"Section {idx + 1}"
+        prompt = (
+            f"Summarize the section of the lecture about '{bookmark_title}' in {BOOKMARK_SUMMARY_WORDS} words or fewer. Highlight the key ideas only."
+        )
+        try:
+            response = ask_question(video_id, prompt)
+            results.append({"summary": response.get("answer", ""), "sources": response.get("sources", [])})
+        except RuntimeError:
+            results.append({"summary": bookmark.get("description", "Summary unavailable."), "sources": []})
+    return results
+
+
+def _render_step_three() -> None:
+    with _step_expander(3, "Step 3 — Summary & quizzes"):
+        video_id = _select_video("Video", key="summary_video_select")
+        style_display = st.selectbox(
+            "Quiz style",
+            [
+                "Mixed",
+                "Multiple Choice Questions (MCQ)",
+                "Open-ended Questions",
+            ],
+            key="quiz_style_display",
+        )
+        style_map = {
+            "Mixed": "mixed",
+            "Multiple Choice Questions (MCQ)": "mcq",
+            "Open-ended Questions": "open",
+        }
+        num_questions = st.slider("Number of questions", 1, 10, 3, key="quiz_question_slider")
+        max_words = st.slider("Summary length (words)", 200, 500, 200, key="summary_word_slider")
+        if st.button("Summary & quizzes", key="summary_quiz_btn"):
+            with st.spinner("Generating summary and quiz..."):
+                if not video_id:
+                    st.warning("Select a video first.")
+                    return
+                try:
+                    st.session_state["summary_by_video"][video_id] = fetch_summary(video_id, max_words=max_words)
+                    st.session_state["quiz_by_video"][video_id] = list(
+                        fetch_quiz(video_id, num_questions=num_questions, style=style_map[style_display])
+                    )
+                    _advance_to_step(3)
+                except RuntimeError as exc:
+                    st.error(str(exc))
+        summary_payload = st.session_state["summary_by_video"].get(video_id)
+        quiz_items = st.session_state["quiz_by_video"].get(video_id)
+        bookmark_summaries = st.session_state["bookmark_summaries_by_video"].get(video_id, [])
+        if summary_payload or quiz_items:
+            overall_tab, quiz_tab = st.tabs(["Overall", "Quiz"])
+            with overall_tab:
+                if summary_payload:
+                    summary_text = summary_payload.get("summary", "")
+                    st.write(summary_text)
+                    if summary_text.strip() == "Insufficient context.":
+                        st.warning("If you see 'Insufficient context.', the lecture may not be fully processed yet—try re-running ingestion or checking the video's status.")
+                else:
+                    st.info("Run the summary to populate this tab.")
+            with quiz_tab:
+                if quiz_items:
+                    for idx, item in enumerate(quiz_items, start=1):
+                        st.markdown(f"**Q{idx}: {item.get('question')}**")
+                        choices = item.get("choices") or []
+                        for choice in choices:
+                            st.write(f"- {choice}")
+                        st.write(f"**Answer:** {item.get('answer')}")
+                        if item.get("explanation"):
+                            st.caption(item["explanation"])
+                else:
+                    st.info("Generate a quiz to populate this tab.")
+
+
+def _render_step_four() -> None:
+    with _step_expander(5, "Step 5 — Smart Search (Optional)"):
+        video_id = _select_video("Video", key="search_video_select")
+        query = st.text_area(
+            "Query",
+            key="semantic_query_input",
+            height=80,
+            placeholder="Locate the key segments in the video that discusses the core subject of the video (please be as specific as possible)."
+        )
+        st.caption("Enter a natural language query to find relevant segments in the video. For example, you can ask for key topics, explanations, or specific concepts. Please be as specific as possible in your query.")
+        limit = st.slider("Results", 1, 10, 4, key="semantic_limit_slider")
+        st.caption("The number of top matching segments to display based on your query.")
+        if st.button("Search", key="semantic_search_btn"):
+            with st.spinner("Searching..."):
+                if not video_id or not query:
+                    st.warning("Provide a video and query.")
+                    return
+                try:
+                    results = list(semantic_search(query, video_id, limit))
+                    st.session_state["search_results_by_video"][video_id] = results
+                    _advance_to_step(4)
+                except RuntimeError as exc:
+                    st.error(str(exc))
+
+        results = st.session_state["search_results_by_video"].get(video_id) or []
+        for item in results:
+            ts = int(item.get("timestamp_seconds") or 0)
+            ts_str = seconds_to_timestamp(ts)
+            link = youtube_timestamp_link(video_id, ts)
+            st.markdown(f"`{ts_str}` {item.get('text', '')}")
+            st.markdown(f"[Open clip ↗]({link})")
+
+
+def _render_step_five() -> None:
+    with _step_expander(4, "Step 4 — Q&A"):
+        video_id = _select_video("Video", key="qa_video_select")
+        question = st.text_area(
+            "Question",
+            key="qa_question_input",
+            height=80,
+            placeholder="Ask a grounded question"
+        )
+        if st.button("Run", key="qa_generate_btn"):
+            if not video_id or not question:
+                st.warning("Provide a question and select a video.")
+                return
+            try:
+                response = ask_question(video_id, question)
+                history = st.session_state["qa_history_by_video"].setdefault(video_id, [])
+                history.append({"question": question, "answer": response.get("answer", ""), "sources": response.get("sources", [])})
+                _advance_to_step(5)
+            except RuntimeError as exc:
+                st.error(str(exc))
+
+        history = st.session_state["qa_history_by_video"].get(video_id) or []
+        if history:
+            st.markdown("#### Multi-turn Q&A History")
+            for idx, item in enumerate(history, start=1):
+                st.markdown(f"<div style='background:#FFF0F5;border-radius:10px;padding:14px 14px 10px 14px;margin-bottom:12px;border:1px solid #F63366;'>"
+                            f"<b style='color:#F63366'>Q{idx}:</b> <span style='color:#D72660;font-weight:500'>{item.get('question')}</span><br>"
+                            f"<b style='color:#F63366'>A{idx}:</b> <span style='color:#333'>{item.get('answer', '')}</span>"
+                            "</div>", unsafe_allow_html=True)
+                if item.get("sources"):
+                    with st.expander("Sources", expanded=False):
+                        for source in item["sources"]:
+                            ts = int(source.get("timestamp_seconds") or 0)
+                            ts_str = source.get("timestamp") or seconds_to_timestamp(ts)
+                            link = youtube_timestamp_link(source.get("video_id", video_id), ts)
+                            st.write(f"[{ts_str}] {source.get('text', '')}")
+                            st.markdown(f"[Open clip ↗]({link})")
+
+
+def home_page() -> None:
+    st.markdown("""
+        <div style='display: flex; align-items: center;'>
+            <span style='font-size: 2.5rem; margin-right: 0.5rem;'>📚</span>
+            <span style='font-size: 2.2rem; font-weight: bold;'>Chaptly AI</span>
+        </div>
+    """, unsafe_allow_html=True)
+    st.markdown("<span style='font-size:1.3rem;color:#F63366;font-weight:500;'>Your AI-powered YouTube study companion</span>", unsafe_allow_html=True)
+    st.caption("Chaptly AI: Transcribe, Summarize, Generate Bookmarks, Quizzes and Q&A.")
+    _init_session_state()
+    _render_sidebar_stepper()
+    _render_api_status()
+    _render_step_one()
+    _render_step_two()
+    _render_step_three()
+    _render_step_five()
+    _render_step_four()
+
+
+if __name__ == "__main__":
+    st.set_page_config(page_title="Chaptly AI", page_icon="📚", layout="wide")
+    home_page()

src/utils/__init__.py

@@ -0,0 +1 @@
+"""Utility helpers for the Streamlit frontend."""

src/utils/helpers.py

@@ -0,0 +1,317 @@
+"""Helper utilities for the Streamlit front-end."""
+
+from __future__ import annotations
+
+import base64
+import hashlib
+import hmac
+import json
+import os
+import time
+from dataclasses import dataclass
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Iterable, Optional
+
+import requests
+import streamlit as st
+from botocore.exceptions import ClientError
+import boto3
+
+from utils.settings import HF_IMG_BASE_URL
+
+DEFAULT_TIMEOUT = 30
+_URL_ENV_KEY = "CHAPTLY_API_URL"
+_API_KEY_ENV_KEY = "CHAPTLY_API_KEY"
+_HMAC_SECRET_ENV_KEY = "CHAPTLY_API_SECRET"
+_S3_BUCKET_ENV_KEY = "CHAPTLY_S3_BUCKET"
+_AWS_REGION_ENV_KEY = "AWS_REGION"
+_SIG_HEADER = "X-Signature"
+_TS_HEADER = "X-Timestamp"
+
+DEFAULT_S3_BUCKET = "chaptly-rag"
+DEFAULT_AWS_REGION = "ap-southeast-1"
+IMAGE_REQUEST_TIMEOUT = 10
+_IMG_DIR = Path(__file__).resolve().parents[2] / "img"
+
+
+@dataclass(frozen=True)
+class APIConfig:
+	base_url: str
+	api_key: str
+	hmac_secret: str
+
+
+def _get_secret_or_env(key: str, default: str = "") -> str:
+	return os.environ.get(key, default).strip()
+
+
+@st.cache_resource(show_spinner=False)
+def _get_s3_client():
+	region = _get_secret_or_env(_AWS_REGION_ENV_KEY, DEFAULT_AWS_REGION) or None
+	return boto3.client("s3", region_name=region)
+
+
+def _get_s3_bucket_name() -> str:
+	return _get_secret_or_env(_S3_BUCKET_ENV_KEY, DEFAULT_S3_BUCKET) or DEFAULT_S3_BUCKET
+
+
+@st.cache_data(show_spinner=False)
+def get_api_config() -> APIConfig:
+	"""Return the API URL/key pair, cached for the session."""
+	base = _get_secret_or_env(_URL_ENV_KEY)
+	api_key = _get_secret_or_env(_API_KEY_ENV_KEY)
+	secret = _get_secret_or_env(_HMAC_SECRET_ENV_KEY)
+	return APIConfig(base_url=base.rstrip("/"), api_key=api_key, hmac_secret=secret)
+
+
+def _request(
+	method: str,
+	path: str,
+	*,
+	params: Optional[Dict[str, Any]] = None,
+	json_payload: Optional[Dict[str, Any]] = None,
+) -> Dict[str, Any]:
+	config = get_api_config()
+	if not config.base_url:
+		raise RuntimeError("CHAPTLY_API_URL is not configured. Set it in Streamlit secrets or environment.")
+	if not config.api_key:
+		raise RuntimeError("CHAPTLY_API_KEY is not configured. Set it in Streamlit secrets or environment.")
+	if not config.hmac_secret:
+		raise RuntimeError("CHAPTLY_API_SECRET is not configured. Set it in Streamlit secrets or environment.")
+
+	url = f"{config.base_url}{path}"
+	body_bytes = _serialize_json(json_payload) if json_payload is not None else b""
+	if json_payload is not None:
+		headers = {"Content-Type": "application/json"}
+	else:
+		headers = {}
+	headers["X-API-Key"] = config.api_key
+	_timestamp, _signature = _sign_request(method, path, body_bytes, config.hmac_secret)
+	headers[_TS_HEADER] = _timestamp
+	headers[_SIG_HEADER] = _signature
+
+	try:
+		resp = requests.request(
+			method,
+			url,
+			headers=headers,
+			params=params,
+			data=body_bytes if body_bytes else None,
+			timeout=DEFAULT_TIMEOUT,
+		)
+	except requests.RequestException as exc:
+		raise RuntimeError(f"Network error contacting API: {exc}") from exc
+
+	if resp.status_code >= 400:
+		error_detail: Any
+		try:
+			error_detail = resp.json()
+		except ValueError:
+			error_detail = resp.text
+		message = _extract_error_message(error_detail)
+		raise RuntimeError(f"API error ({resp.status_code}): {message}")
+
+	try:
+		return resp.json()
+	except ValueError as exc:
+		raise RuntimeError("API returned invalid JSON") from exc
+
+
+def _serialize_json(payload: Dict[str, Any]) -> bytes:
+	return json.dumps(payload, ensure_ascii=False, separators=(",", ":"), sort_keys=True).encode("utf-8")
+
+
+def _sign_request(method: str, path: str, body: bytes, secret: str) -> tuple[str, str]:
+	timestamp = str(int(time.time()))
+	body_hash = hashlib.sha256(body).hexdigest()
+	canonical = "\n".join([
+		method.upper(),
+		path,
+		body_hash,
+		timestamp,
+	])
+	signature = hmac.new(secret.encode("utf-8"), canonical.encode("utf-8"), hashlib.sha256).hexdigest()
+	return timestamp, signature
+
+
+def _extract_error_message(error_detail: Any) -> str:
+	if isinstance(error_detail, dict):
+		if "detail" in error_detail and isinstance(error_detail["detail"], str):
+			return error_detail["detail"]
+		if "message" in error_detail and isinstance(error_detail["message"], str):
+			return error_detail["message"]
+	return str(error_detail)
+
+
+def ping_health() -> Dict[str, Any]:
+	return _request("GET", "/health")
+
+
+def start_ingestion(video_url: str) -> Dict[str, Any]:
+	return _request("POST", "/videos/process", params={"url": video_url})
+
+
+def fetch_job_status(job_id: str) -> Dict[str, Any]:
+	return _request("GET", f"/videos/process/{job_id}")
+
+
+def fetch_bookmarks(video_id: str, *, max_sections: int, min_sections: int) -> Iterable[Dict[str, Any]]:
+	payload = _request(
+		"GET",
+		f"/videos/{video_id}/bookmarks",
+		params={"max_sections": max_sections, "min_sections": min_sections},
+	)
+	return payload
+
+
+def ask_question(video_id: str, query: str, limit: int = 4) -> Dict[str, Any]:
+	return _request(
+		"POST",
+		f"/videos/{video_id}/qa",
+		json_payload={"query": query, "limit": limit},
+	)
+
+
+def fetch_summary(video_id: str, max_words: int = 150) -> Dict[str, Any]:
+	return _request("GET", f"/videos/{video_id}/summary", params={"max_words": max_words})
+
+
+def fetch_quiz(video_id: str, num_questions: int = 3, style: str = "mixed") -> Iterable[Dict[str, Any]]:
+	return _request(
+		"GET",
+		f"/videos/{video_id}/quiz",
+		params={"num_questions": num_questions, "style": style},
+	)
+
+
+def semantic_search(query: str, video_id: str, limit: int = 4) -> Iterable[Dict[str, Any]]:
+	return _request(
+		"POST",
+		"/search",
+		json_payload={"query": query, "video_id": video_id, "limit": limit},
+	)
+
+
+def seconds_to_timestamp(total_seconds: int) -> str:
+	minutes, seconds = divmod(max(total_seconds, 0), 60)
+	return f"{minutes:02d}:{seconds:02d}"
+
+
+def youtube_timestamp_link(video_id: str, timestamp_seconds: Optional[int]) -> str:
+	seconds = int(timestamp_seconds or 0)
+	return f"https://www.youtube.com/watch?v={video_id}&t={seconds}s"
+
+
+def render_error(message: str) -> None:
+	st.error(message)
+
+
+def render_success(message: str) -> None:
+	st.success(message)
+
+
+def _build_remote_image_url(filename: str) -> str:
+	sanitized = filename.lstrip("/")
+	return f"{HF_IMG_BASE_URL.rstrip('/')}/{sanitized}"
+
+
+def get_remote_image_url(filename: str) -> str:
+	"""Public helper to retrieve the remote URL that serves the given asset."""
+	return _build_remote_image_url(filename)
+
+
+def resolve_image_source(filename: str) -> str:
+	"""Return a local path if available, otherwise fall back to the remote asset URL."""
+	path = _IMG_DIR / filename
+	if path.exists():
+		return str(path)
+	return _build_remote_image_url(filename)
+
+
+def _load_remote_image_bytes(filename: str) -> Optional[bytes]:
+	url = _build_remote_image_url(filename)
+	try:
+		resp = requests.get(url, timeout=IMAGE_REQUEST_TIMEOUT)
+		if resp.status_code == 200:
+			return resp.content
+	except requests.RequestException:
+		return None
+	return None
+
+
+@lru_cache(maxsize=128)
+def get_image_base64(filename: str) -> Optional[str]:
+	"""Return a base64-encoded representation of an image from disk or remote storage."""
+	path = _IMG_DIR / filename
+	if path.exists():
+		try:
+			return base64.b64encode(path.read_bytes()).decode("utf-8")
+		except OSError:
+			pass
+	data = _load_remote_image_bytes(filename)
+	if data is None:
+		return None
+	return base64.b64encode(data).decode("utf-8")
+
+
+def _object_exists(key: str) -> bool:
+	s3 = _get_s3_client()
+	bucket = _get_s3_bucket_name()
+	try:
+		s3.head_object(Bucket=bucket, Key=key)
+		return True
+	except ClientError as exc:
+		if exc.response.get("Error", {}).get("Code") in {"404", "NoSuchKey"}:
+			return False
+		raise RuntimeError(f"Unable to read S3 object {key}: {exc}") from exc
+
+
+def is_video_cached(video_id: str) -> bool:
+	if not video_id:
+		return False
+	video_prefix = video_id.strip().strip("/")
+	chunks_key = f"{video_prefix}/chunks.json"
+	embeddings_key = f"{video_prefix}/embeddings.npy"
+	return _object_exists(chunks_key) and _object_exists(embeddings_key)
+
+
+def _load_video_metadata(video_id: str) -> Dict[str, Any]:
+	s3 = _get_s3_client()
+	bucket = _get_s3_bucket_name()
+	artifacts_key = f"{video_id}/artifacts.json"
+	try:
+		obj = s3.get_object(Bucket=bucket, Key=artifacts_key)
+		data = json.loads(obj["Body"].read())
+		return {
+			"video_id": video_id,
+			"title": data.get("title") or video_id,
+			"channel_name": data.get("channel") or data.get("channel_name", ""),
+			"thumbnail_url": data.get("thumbnail_url"),
+			"url": data.get("url") or f"https://www.youtube.com/watch?v={video_id}",
+		}
+	except ClientError:
+		return {
+			"video_id": video_id,
+			"title": video_id,
+			"channel_name": "",
+			"thumbnail_url": None,
+			"url": f"https://www.youtube.com/watch?v={video_id}",
+		}
+
+
+@st.cache_data(show_spinner=False, ttl=300)
+def list_cached_videos() -> list[Dict[str, Any]]:
+	s3 = _get_s3_client()
+	bucket = _get_s3_bucket_name()
+	paginator = s3.get_paginator("list_objects_v2")
+	items: list[Dict[str, Any]] = []
+	for page in paginator.paginate(Bucket=bucket, Delimiter="/"):
+		for prefix in page.get("CommonPrefixes", []):
+			video_id = prefix.get("Prefix", "").rstrip("/")
+			if not video_id or video_id == "lambda":
+				continue
+			if is_video_cached(video_id):
+				metadata = _load_video_metadata(video_id)
+				items.append(metadata)
+	return sorted(items, key=lambda item: item.get("title", ""))

src/utils/settings.py

@@ -0,0 +1,50 @@
+# pylint: disable=missing-module-docstring
+
+import os
+
+# List of approved YouTube channels from Terms of Use
+APPROVED_CHANNELS = [
+    # Academic Institutions
+    "MIT OpenCourseWare", "stanford online", "harvard", "YaleCourses",
+    
+    # Educational Platforms
+    "Khan Academy", "Coursera", "edx", "udacity",
+    
+    # Educational Content Creators
+    "crashcourse", "TED-Ed", "TED", "TEDx Talks", "3blue1brown", "numberphile",
+    "Computerphile", "SciShow", "veritasium", "minutephysics",
+    "programmingwithmosh", "freecodecamp.org"
+]
+
+APPROVED_CHANNEL_IDS = {
+    "MIT OpenCourseWare"    : "UCEBb1b_L6zDS3xTUrIALZOw",
+    "stanford online"       : "UCBa5G_ESCn8Yd4vw5U-gIcg",
+    "Stanford Online"       : "UCBa5G_ESCn8Yd4vw5U-gIcg",
+    "harvard"               : "UCiR_s0m_o_6x4Qv9F9Kj_1A",
+    "Harvard"               : "UCiR_s0m_o_6x4Qv9F9Kj_1A",
+    "YaleCourses"           : "UC-Lg4S121Qk2pPqB09yD3Xg",
+    "Khan Academy"          : "UC4a-Gbdw7KEp5o7L5d_22wQ",
+    "Coursera"              : "UCF0m58Cj3iC_qD1L7I2d26Q",
+    "edx"                   : "UCLx1L9tN206S8_61xW_B2qA",
+    "udacity"               : "UClC7-L9_h7qT9B02D0HhBKA",
+    "crashcourse"           : "UCX6b17PVsYBQ0ip5gyeme-Q",
+    "TED-Ed"                : "UC_y-x5QvP93pBqD41zE3e3A",
+    "TED"                   : "UCAuUUnT6oFdlLEx-H-v7Y0A",
+    "TEDx Talks"            : "UCsooa4yRKGN_zEE8iknghZA",
+    "3blue1brown"           : "UCYO_jab_esuFRV4b17AJtAw",
+    "numberphile"           : "UCsT0gAGB4I5rR_n_tM8Qz-A",
+    "Computerphile"         : "UC9-y-6csu5WGm29I7JiwpnA",
+    "SciShow"               : "UCZYTClx2T1of7BRZ86-8fow",
+    "veritasium"            : "UCHnyfMqiRRG1u-2MsSQLbXA",
+    "minutephysics"         : "UCUHW94eEFW7hkUMVaZz4eDg",
+    "programmingwithmosh"   : "UCWv7g_L_jL4L_gT43hS-x2A",
+    "freecodecamp.org"      : "UC8butISFwT-Wl7EV0hUK0BQ"
+}
+
+HF_SPACE_ID = os.getenv("HF_SPACE_ID", "Jing997/chaptly")
+HF_SPACE_BRANCH = os.getenv("HF_SPACE_BRANCH", "main")
+HF_ASSET_BASE_URL = os.getenv(
+    "HF_ASSET_BASE_URL",
+    f"https://huggingface.co/spaces/{HF_SPACE_ID}/resolve/{HF_SPACE_BRANCH}",
+)
+HF_IMG_BASE_URL = os.getenv("HF_IMG_BASE_URL", f"{HF_ASSET_BASE_URL}/img")