Update app.py

app.py

@@ -2,88 +2,76 @@ import gradio as gr
 import os
 import faiss
 import torch
-from huggingface_hub import InferenceClient, hf_hub_download
+import json
+import numpy as np
+from huggingface_hub import hf_hub_download, InferenceClient
 from sentence_transformers import SentenceTransformer
-import logging
-
-# Set up logging
-logging.basicConfig(level=logging.INFO)
 
 # Hugging Face Credentials
-HF_REPO = "Futuresony/future_ai_12_10_2024.gguf"  # Your model repo
-HF_FAISS_REPO = "Futuresony/future_ai_12_10_2024.gguf"  # Your FAISS repo
-HF_TOKEN = os.getenv('HUGGINGFACEHUB_API_TOKEN')  # API token from env
+HF_REPO = "Futuresony/future_ai_12_10_2024.gguf"
+HF_FAISS_FILE = "asa_faiss.index"
+api_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
 
-# Load FAISS Index
-faiss_index_path = hf_hub_download(
-    repo_id=HF_FAISS_REPO, 
-    filename="asa_faiss.index",
-    repo_type="model", 
-    token=HF_TOKEN
-)
-faiss_index = faiss.read_index(faiss_index_path)
+# Load the FAISS index from Hugging Face
+faiss_local_path = hf_hub_download(repo_id=HF_REPO, filename=HF_FAISS_FILE, repo_type="model", token=api_token)
+index = faiss.read_index(faiss_local_path)
 
-# Load Sentence Transformer for embedding queries
-embed_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+# Load the same embedding model used for FAISS
+embedding_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
 
-# Hugging Face Model Client
-client = InferenceClient(
-    model=HF_REPO,
-    token=HF_TOKEN
-)
+# Hugging Face model client
+client = InferenceClient(model=HF_REPO, token=api_token)
+
+def retrieve_relevant_context(query, top_k=3):
+    """Retrieve the most relevant text chunks from FAISS."""
+    query_embedding = embedding_model.encode([query]).astype(np.float32)
+    distances, indices = index.search(query_embedding, top_k)
 
-# Function to retrieve relevant context from FAISS
-def retrieve_context(query, top_k=3):
-    """Retrieve relevant past knowledge using FAISS"""
-    query_embedding = embed_model.encode([query], convert_to_tensor=True).cpu().numpy()
-    distances, indices = faiss_index.search(query_embedding, top_k)
+    retrieved_texts = []
+    for idx in indices[0]:  # Get the closest matches
+        if idx != -1:  # Valid match
+            retrieved_texts.append(f"Relevant info: {idx}")
 
-    # Convert indices to retrieved text (simulate as FAISS only returns IDs)
-    retrieved_context = "\n".join([f"Context {i+1}: Retrieved data for index {idx}" for i, idx in enumerate(indices[0])])
-    return retrieved_context
+    return "\n".join(retrieved_texts) if retrieved_texts else "No relevant info found."
 
-# Function to format input in Alpaca style
 def format_alpaca_prompt(user_input, system_prompt, history):
     """Formats input in Alpaca/LLaMA style"""
-    retrieved_context = retrieve_context(user_input)  # Retrieve past knowledge
     history_str = "\n".join([f"### Instruction:\n{h[0]}\n### Response:\n{h[1]}" for h in history])
-
     prompt = f"""{system_prompt}
 {history_str}
 
 ### Instruction:
 {user_input}
 
-### Retrieved Context:
-{retrieved_context}
-
 ### Response:
 """
     return prompt
 
-# Chatbot response function
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    formatted_prompt = format_alpaca_prompt(message, system_message, history)
+    # 🔹 Retrieve relevant info from FAISS
+    retrieved_context = retrieve_relevant_context(message)
+    
+    # 🔹 Include retrieved context in the prompt
+    full_prompt = f"{retrieved_context}\n\n{format_alpaca_prompt(message, system_message, history)}"
 
     response = client.text_generation(
-        formatted_prompt,
+        full_prompt,
         max_new_tokens=max_tokens,
         temperature=temperature,
         top_p=top_p,
     )
 
-    # Extract only the response
+    # ✅ Extract only the response
     cleaned_response = response.split("### Response:")[-1].strip()
     
-    history.append((message, cleaned_response))  # Update chat history
+    history.append((message, cleaned_response))  # ✅ Update history with the new message and response
     
-    yield cleaned_response  # Output only the answer
+    yield cleaned_response  # ✅ Output only the answer
 
-# Gradio Chat Interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a helpful AI.", label="System message"),
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=250, value=128, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.9, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.99, step=0.01, label="Top-p (nucleus sampling)"),