chat ui with gradio

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
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+-----END CERTIFICATE-----

requirements.txt

@@ -15,4 +15,6 @@ pypdf==4.2.0
 # ML/AI Dependencies (with CPU-only versions)
 sentence_transformers==2.3.1
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch==2.1.0+cpu
+torch==2.1.0+cpu
+
+gradio

ui/app.py

@@ -0,0 +1,140 @@
+import gradio as gr
+import chromadb
+from typing import List, Dict
+import sys
+from pathlib import Path
+
+project_root = Path(__file__).parent.parent
+sys.path.append(str(project_root))
+from Rag.rag_pipeline import (
+    query_database,
+    generate_response,
+    enhance_query_with_history,
+    update_conversation_history,
+    process_and_add_new_files
+)
+
+INTRODUCTION = """
+# 🧠 Welcome to HubermanBot!
+
+I am your AI assistant trained on Andrew Huberman's podcast content. My knowledge base includes detailed information about:
+
+- 🎯 Peak Performance & Focus
+- 😴 Sleep Science & Optimization
+- 🏋️ Physical Fitness & Recovery
+- 🧘 Mental Health & Stress Management
+- 🧪 Neuroscience & Biology
+- 💪 Habit Formation & Behavior Change
+
+For each response, I'll provide:
+- Detailed answers based on podcast content
+- Direct source links to specific episodes
+- Scientific context when available
+
+Ask me anything about these topics, and I'll help you find relevant information from the Huberman Lab Podcast!
+
+Example questions you might ask:
+- "What does Dr. Huberman recommend for better sleep?"
+- "How can I improve my focus and concentration?"
+- "What are the best practices for morning routines?"
+"""
+
+
+def format_youtube_url(filename: str) -> str:
+    """Convert filename to YouTube URL"""
+    # Extract video ID by removing the timestamp and .txt extension
+    video_id = filename.split('_')[0]
+    return f"https://www.youtube.com/watch?v={video_id}"
+
+
+class RAGChatInterface:
+    def __init__(self, transcripts_folder_path: str, collection):
+        self.transcripts_folder_path = transcripts_folder_path
+        self.collection = collection
+        self.conversation_history: List[Dict[str, str]] = []
+
+    def process_query(self, message: str, history: List[List[str]]) -> str:
+        """Process a single query and return the response"""
+        # Convert Gradio history format to our conversation history format
+        self.conversation_history = [
+            {"user": user_msg, "bot": bot_msg}
+            for user_msg, bot_msg in history
+        ]
+
+        # Enhance query with conversation history
+        query_with_history = enhance_query_with_history(message, self.conversation_history)
+
+        # Get relevant documents
+        retrieved_docs, metadatas = query_database(self.collection, query_with_history)
+
+        if not retrieved_docs:
+            return "I apologize, but I couldn't find any relevant information about that in my knowledge base. Could you try rephrasing your question or ask about a different topic covered in the Huberman Lab Podcast?"
+
+        # Generate response
+        source_links = [meta["source"] for meta in metadatas]
+        response = generate_response(
+            self.conversation_history,
+            message,
+            retrieved_docs,
+            source_links
+        )
+
+        # Remove duplicate sources and convert to YouTube URLs
+        unique_sources = list(set(source_links))
+        youtube_urls = [format_youtube_url(source) for source in unique_sources]
+
+        # Format response with markdown for better readability
+        formatted_response = f"{response}\n\n---\n📚 **Source Episodes:**\n"
+        for url in youtube_urls:
+            formatted_response += f"- {url}\n"
+
+        return formatted_response
+
+
+def create_interface(transcripts_folder_path: str, collection) -> gr.Interface:
+    """Create and configure the Gradio interface"""
+    # Initialize the RAG chat interface
+    rag_chat = RAGChatInterface(transcripts_folder_path, collection)
+
+    # Create the Gradio interface with custom styling
+    interface = gr.ChatInterface(
+        fn=rag_chat.process_query,
+        title="🧠 HubermanBot - Your Neuroscience & Wellness AI Assistant",
+        description=INTRODUCTION,
+        examples=[
+            "What are Dr. Huberman's top recommendations for better sleep?",
+            "How does sunlight exposure affect our circadian rhythm?",
+            "What supplements does Dr. Huberman recommend for focus?",
+            "What are the best practices for morning routines according to Dr. Huberman?",
+            "How can I optimize my workout recovery based on neuroscience?",
+        ],
+        theme=gr.themes.Soft(
+            primary_hue="indigo",
+            secondary_hue="blue",
+        )
+    )
+
+    return interface
+
+
+def main():
+    # Get absolute path for ChromaDB
+    project_root = Path(__file__).parent.parent
+    chromadb_path = project_root / "Rag" / "chromadb.db"
+
+    client = chromadb.PersistentClient(path=str(chromadb_path))
+    collection = client.get_or_create_collection(name="yt_transcript_collection")
+
+    # Use absolute path for transcripts folder too
+    transcripts_folder_path = project_root / "Data" / "transcripts"
+
+    # Process any new files
+    process_and_add_new_files(str(transcripts_folder_path), collection)
+
+    # Create and launch the interface
+    interface = create_interface(str(transcripts_folder_path), collection)
+    interface.launch(share=True, server_port=7860)
+
+
+if __name__ == "__main__":
+    main()