Update app.py

app.py

@@ -1,93 +1,53 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1f0fddef-d467-4a41-b0d3-0107c8393190",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import gradio as gr\n",
-    "from transformers import pipeline\n",
-    "import torch\n",
-    "import scipy\n",
-    "import sentencepiece\n",
-    "from google.protobuf import text_format\n",
-    "import re\n",
-    "\n",
-    "# Load sentiment analysis pipeline\n",
-    "sentiment_model = pipeline(\"sentiment-analysis\")\n",
-    "\n",
-    "# Function to analyze text and calculate risk\n",
-    "def analyze_text(text):\n",
-    "    result = sentiment_model(text)[0]\n",
-    "    sentiment = result['label']\n",
-    "    score = result['score']\n",
-    "\n",
-    "    # Simple risk scoring logic\n",
-    "    risk_score = score * (1.5 if sentiment == \"NEGATIVE\" else 1.0)\n",
-    "    escalation = risk_score > 0.7  # threshold\n",
-    "    return sentiment, risk_score, escalation\n",
-    "\n",
-    "# Process input for Gradio\n",
-    "def process_input(text):\n",
-    "    sentiment, risk_score, escalation = analyze_text(text)\n",
-    "    return sentiment, round(risk_score, 2), \"Yes\" if escalation else \"No\"\n",
-    "\n",
-    "title = \"SocialAegis MVP\"\n",
-    "description = \"\"\"\n",
-    "A sentiment-based escalation engine to detect emotional volatility in social media posts.\n",
-    "\"\"\"\n",
-    "\n",
-    "iface = gr.Interface(\n",
-    "    fn=process_input,\n",
-    "    inputs=gr.Textbox(lines=4, placeholder=\"Enter social media text here...\"),\n",
-    "    outputs=[\n",
-    "        gr.Label(label=\"Sentiment\"),\n",
-    "        gr.Label(label=\"Risk Score\"),\n",
-    "        gr.Label(label=\"Escalation Triggered\")\n",
-    "    ],\n",
-    "    title=title,\n",
-    "    description=description,\n",
-    "    examples=[\n",
-    "        [\"I am so frustrated with this service!\"],\n",
-    "        [\"I had an amazing experience, thank you!\"],\n",
-    "        [\"This is unacceptable, I will report this.\"]\n",
-    "    ]\n",
-    ")\n",
-    "\n",
-    "if __name__ == \"__main__\":\n",
-    "    iface.launch()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4d59bd58-8c46-4707-81b4-bf7780d27812",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
+# app.py
+import gradio as gr
+from transformers import pipeline
+import torch
+import scipy
+import sentencepiece
+from google.protobuf import text_format
+import re
+
+# Load sentiment analysis pipeline
+sentiment_model = pipeline("sentiment-analysis")
+
+# Function to analyze text and calculate risk
+def analyze_text(text):
+    result = sentiment_model(text)[0]
+    sentiment = result['label']
+    score = result['score']
+
+    # Simple risk scoring logic
+    risk_score = score * (1.5 if sentiment == "NEGATIVE" else 1.0)
+    escalation = risk_score > 0.7  # threshold
+    return sentiment, risk_score, escalation
+
+# Process input for Gradio
+def process_input(text):
+    sentiment, risk_score, escalation = analyze_text(text)
+    return sentiment, round(risk_score, 2), "Yes" if escalation else "No"
+
+# Gradio app configuration
+title = "SocialAegis MVP"
+description = """
+A sentiment-based escalation engine to detect emotional volatility in social media posts.
+"""
+
+iface = gr.Interface(
+    fn=process_input,
+    inputs=gr.Textbox(lines=4, placeholder="Enter social media text here..."),
+    outputs=[
+        gr.Label(label="Sentiment"),
+        gr.Label(label="Risk Score"),
+        gr.Label(label="Escalation Triggered")
+    ],
+    title=title,
+    description=description,
+    examples=[
+        ["I am so frustrated with this service!"],
+        ["I had an amazing experience, thank you!"],
+        ["This is unacceptable, I will report this."]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()