Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 import os
-import time
 from langchain_community.document_loaders import PyPDFLoader
 from langchain_text_splitters import RecursiveCharacterTextSplitter
 from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
@@ -8,176 +7,133 @@ from langchain_community.vectorstores import FAISS
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain_core.prompts import ChatPromptTemplate
 
-# --- 1. Model Setup ---
-HF_TOKEN = os.environ.get("HF_TOKEN")
+# --- 1. Model Setup using HF Inference API ---
+# Get the HF token from environment variables (set in Space secrets)
+HF_TOKEN = os.environ.get("HF_TOKEN", "")
 
-# Setup the Model (Llama 3 8B via API)
-if HF_TOKEN:
-    llm = HuggingFaceEndpoint(
-        repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
-        temperature=0.5,
-        max_new_tokens=4096,
-        huggingfacehub_api_token=HF_TOKEN,
-    )
-else:
-    llm = None
+if not HF_TOKEN:
+    print("⚠️ Warning: HF_TOKEN not set. The app may not work properly.")
+    print("Please add your Hugging Face token in Space Settings > Repository secrets")
+
+llm = HuggingFaceEndpoint(
+    repo_id="meta-llama/Meta-Llama-3-8B-Instruct",
+    temperature=0.7,
+    max_new_tokens=2000,
+    huggingfacehub_api_token=HF_TOKEN
+)
 
-# --- 2. Logic ---
+# --- 2. The Core Logic ---
 def generate_question_paper(pdf_file, difficulty, num_questions):
-    # Error Handling for missing token
+    if not pdf_file:
+        return "❌ Please upload a PDF file first."
+    
     if not HF_TOKEN:
-        return "⚠️ Error: HF_TOKEN is missing. Please add it in Space Settings > Secrets.", "Error"
+        return "❌ Error: HF_TOKEN not configured. Please add your Hugging Face token in Space Settings > Repository secrets."
     
-    if not pdf_file:
-        return "⚠️ Please upload a PDF file first.", "Input Error"
-
     try:
-        # Progress updates (simulated for UI feedback)
-        yield "📂 Reading PDF...", "Processing"
+        # A. Load PDF
         loader = PyPDFLoader(pdf_file.name)
         pages = loader.load()
         
         if not pages:
-            return "❌ Error: The PDF appears to be empty or unreadable.", "Error"
-
-        yield f"✂️ Splitting {len(pages)} pages...", "Processing"
+            return "❌ Error: Could not extract text from PDF. Please ensure it's a valid PDF with text content."
+        
+        # B. Split Text
         text_splitter = RecursiveCharacterTextSplitter(
             chunk_size=1000,
             chunk_overlap=100
         )
         chunks = text_splitter.split_documents(pages)
-
-        yield "🧠 Analyzing content...", "Embedding"
+        
+        # C. Vector Store (FAISS)
         embeddings = FastEmbedEmbeddings()
         vector_store = FAISS.from_documents(chunks, embeddings)
         
-        yield "🔍 Retrieving key concepts...", "Retrieving"
-        retriever = vector_store.as_retriever(search_kwargs={"k": 5})
-        context_docs = retriever.invoke("Summary of key topics, definitions, and important details")
+        # D. Retrieve Context
+        retriever = vector_store.as_retriever(search_kwargs={"k": 7})
+        context_docs = retriever.invoke("Key concepts and definitions")
         context_text = "\n\n".join([doc.page_content for doc in context_docs])
-
-        # Prompt
-        template = """
-        You are an expert academic examiner. Create a rigorous Question Paper based ONLY on the provided context.
-        
-        CONTEXT:
-        {context}
-        
-        INSTRUCTIONS:
-        - Difficulty: {difficulty}
-        - Total Questions: {num_questions}
-        - Structure:
-          * Part A: Multiple Choice ({num_questions} questions)
-          * Part B: Short Answer (2 questions)
-          * Part C: Essay/Long Answer (1 question)
-        - Include an "Answer Key" section at the very bottom.
         
-        OUTPUT FORMAT:
-        Return valid Markdown. Use bold headers. Do not output conversational filler.
-        """
+        # E. Prompt
+        template = """You are an expert academic examiner. Create a formal Question Paper based ONLY on the context provided below.
+
+CONTEXT:
+{context}
+
+INSTRUCTIONS:
+- Difficulty: {difficulty}
+- Total Questions: {num_questions}
+- Format:
+  Section A: Multiple Choice Questions (MCQs)
+  Section B: Short Answer Questions
+  Section C: Long Answer/Essay Questions
+- Provide the Answer Key for MCQs at the very end.
+
+Do not output conversational text. Output ONLY the exam paper in a well-formatted structure."""
         
         prompt = ChatPromptTemplate.from_template(template)
-        chain = prompt | llm
         
-        yield "✨ Generating final paper...", "Generating"
+        # F. Generate
+        chain = prompt | llm
         response = chain.invoke({
             "context": context_text, 
             "difficulty": difficulty,
             "num_questions": num_questions
         })
         
-        yield response, "Complete"
+        return response
 
     except Exception as e:
-        yield f"❌ System Error: {str(e)}", "Failed"
-
-# --- 3. Custom UI ---
-# Custom CSS for a professional look
-custom_css = """
-.container { max-width: 1200px; margin: auto; padding-top: 20px; }
-.header-text { text-align: center; font-family: 'Helvetica', sans-serif; }
-.header-text h1 { color: #2D3748; font-size: 3em; margin-bottom: 0px; }
-.header-text h3 { color: #718096; font-weight: 300; }
-.submit-btn { background: linear-gradient(90deg, #4F46E5 0%, #7C3AED 100%) !important; color: white !important; border: none !important; }
-.status-bar { border: 1px solid #e2e8f0; background: #f7fafc; padding: 10px; border-radius: 8px; color: #4a5568; }
-"""
-
-theme = gr.themes.Soft(
-    primary_hue="indigo",
-    secondary_hue="blue",
-    neutral_hue="slate",
-    text_size="lg"
-)
+        return f"❌ Error processing PDF: {str(e)}\n\nPlease check:\n1. PDF is valid and contains text\n2. HF_TOKEN is correctly set\n3. You have access to Llama 3"
 
-# UPDATED: theme and css removed from here
-with gr.Blocks(title="AI Exam Gen") as demo:
+# --- 3. The UI ---
+with gr.Blocks(title="AI Question Paper Generator") as demo:
+    gr.Markdown("# 📄 AI Question Paper Generator")
+    gr.Markdown("Powered by **Llama 3 (8B)** via Hugging Face Inference API")
+    gr.Markdown("⚡ Fast • 🎯 Accurate • 📚 Context-Aware")
     
-    with gr.Column(elem_classes="container"):
-        # Header
-        gr.HTML("""
-            <div class="header-text">
-                <h1>📝 AI Question Paper Generator</h1>
-                <h3>Upload study material, get a formatted exam in seconds.</h3>
-            </div>
-        """)
-        
-        with gr.Row(variant="panel", equal_height=True):
-            # Left Column: Inputs
-            with gr.Column(scale=1):
-                gr.Markdown("### 🛠️ Configuration")
-                pdf_input = gr.File(
-                    label="Upload PDF (Study Notes/Book)",
-                    file_types=[".pdf"],
-                    file_count="single",
-                    height=100
+    with gr.Row():
+        with gr.Column(scale=1):
+            pdf_input = gr.File(
+                label="📄 Upload Study Material (PDF)",
+                file_types=[".pdf"]
+            )
+            
+            with gr.Group():
+                difficulty = gr.Radio(
+                    ["Easy", "Medium", "Hard"], 
+                    label="🎚️ Difficulty Level", 
+                    value="Medium"
                 )
-                
-                with gr.Group():
-                    difficulty = gr.Radio(
-                        ["Easy", "Medium", "Hard"], 
-                        label="Difficulty Level", 
-                        value="Medium",
-                        info="Adjusts complexity of questions."
-                    )
-                    
-                    num_questions = gr.Slider(
-                        minimum=5, 
-                        maximum=20, 
-                        value=10, 
-                        step=1, 
-                        label="Number of MCQs",
-                        info="How many objective questions?"
-                    )
-                
-                btn = gr.Button("✨ Generate Question Paper", elem_classes="submit-btn", variant="primary")
-                status = gr.Textbox(label="Status", placeholder="Ready", interactive=False, max_lines=1)
-
-            # Right Column: Output
-            with gr.Column(scale=2):
-                gr.Markdown("### 📄 Generated Exam")
-                output = gr.Markdown(
-                    label="Exam Paper", 
-                    value="_Your generated question paper will appear here..._",
-                    show_copy_button=True,
-                    line_breaks=True
+                num_questions = gr.Slider(
+                    5, 20, value=10, step=1, 
+                    label="📊 Total Questions"
                 )
+            
+            btn = gr.Button("✨ Generate Question Paper", variant="primary", size="lg")
+            
+            gr.Markdown("""
+            ### 📝 Instructions:
+            1. Upload a PDF containing study material
+            2. Select difficulty level
+            3. Choose number of questions
+            4. Click Generate!
+            """)
+        
+        with gr.Column(scale=2):
+            output = gr.Markdown(label="Generated Question Paper")
 
-    # Logic
     btn.click(
         fn=generate_question_paper,
         inputs=[pdf_input, difficulty, num_questions],
-        outputs=[output, status]
-    )
-
-    # Footer
-    gr.Markdown(
-        """
-        <div style="text-align: center; color: #a0aec0; margin-top: 40px;">
-            Powered by Llama 3 • LangChain • Hugging Face
-        </div>
-        """
+        outputs=output
     )
+    
+    gr.Markdown("""
+    ---
+    **Note:** This app requires a Hugging Face token with access to Llama 3. 
+    Set `HF_TOKEN` in your Space's repository secrets.
+    """)
 
 if __name__ == "__main__":
-    # UPDATED: theme and css moved here for Gradio 6 compatibility
-    demo.launch(theme=theme, css=custom_css)
+    demo.launch()