Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import pinecone
+from sentence_transformers import SentenceTransformer
+
+# ✅ Initialize Pinecone
+pc = pinecone.Pinecone(api_key="pcsk_6awTRp_rSsr7eom3bSZXZZcnDLDwc87RnpU2Sp9WEzyEFdEj2TtiyRwjEfnaXswVjGqLi")
+
+# ✅ Define Indexes
+INDEXES = {
+    "YouTube": "youtube-data-index",
+    "Chrome": "chrome-history-index"
+}
+
+# ✅ Model paths (Hugging Face)
+MODEL_PATHS = {
+    "YouTube": "Vishal3041/falcon_finetuned_llm",
+    "Chrome": "Vishal3041/TransNormerLLM_finetuned"
+}
+
+# ✅ Load Sentence Transformer for correct embedding size (384)
+embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# ✅ Function to load model dynamically
+def load_model(model_name):
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name, trust_remote_code=True, device_map="auto"
+    )
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    return model, tokenizer
+
+# ✅ Function to query Pinecone
+def query_pinecone(query, app_selected):
+    """ Retrieves the most relevant results from Pinecone. """
+    index_name = INDEXES[app_selected]
+    index = pc.Index(index_name)
+
+    # Generate embedding for the query
+    query_vector = embedding_model.encode(query).tolist()
+
+    # Query Pinecone for relevant results
+    results = index.query(
+        vector=query_vector,
+        top_k=5,
+        include_metadata=True
+    )
+
+    # Format results for context
+    context_list = []
+    for res in results.get("matches", []):
+        metadata = res.get("metadata", {})
+        title = metadata.get("Title", "No Title")
+        timestamp = metadata.get("Timestamp", "No Date")
+
+        if app_selected == "Chrome":
+            formatted_entry = f"📌 **{title}**\n   🕒 *Visited on: {timestamp}*"
+        else:
+            watched_at = metadata.get("Watched At", "Unknown Date")
+            video_link = metadata.get("Video Link", "#")
+            formatted_entry = f"🎬 **[{title}]({video_link})**\n   📅 *Watched on: {watched_at}*"
+
+        context_list.append(formatted_entry)
+
+    return "\n\n".join(context_list) if context_list else "No relevant results found."
+
+# ✅ Function to generate response
+def generate_response(query, app_selected):
+    """ Handles RAG pipeline: fetches context + generates LLM response. """
+    
+    # Load correct model from Hugging Face
+    model_name = MODEL_PATHS[app_selected]
+    model, tokenizer = load_model(model_name)
+
+    # Get relevant context
+    context = query_pinecone(query, app_selected)
+
+    # Format input prompt
+    input_text = f"Context: {context}\nUser Question: {query}\nAnswer:"
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+
+    # Generate response
+    output = model.generate(input_ids, max_length=512, do_sample=True, top_p=0.9, temperature=0.7)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+
+    return response
+
+# ✅ Gradio UI
+def gradio_ui(query, app_selected):
+    return generate_response(query, app_selected)
+
+# ✅ Create Gradio Interface
+iface = gr.Interface(
+    fn=gradio_ui,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Type your question..."),
+        gr.Radio(["YouTube", "Chrome"], label="Select Application", value="YouTube")
+    ],
+    outputs="text",
+    title="📌 Personal AI Assistant",
+    description="Chat with your YouTube or Chrome history using AI!"
+)
+
+# ✅ Launch Gradio UI
+if __name__ == "__main__":
+    iface.launch()