Update app.py

app.py

@@ -1,35 +1,67 @@
 import gradio as gr
 import os
-from huggingface_hub import InferenceClient
-from textblob import TextBlob
-import json
-import time
+import faiss
+import torch
+from huggingface_hub import InferenceClient, hf_hub_download
+from sentence_transformers import SentenceTransformer
 import logging
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 
-# Get the API token from the environment variable
-api_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+# 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
 
+# 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 Sentence Transformer for embedding queries
+embed_model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+
+# Hugging Face Model Client
 client = InferenceClient(
-    model="Futuresony/future_ai_12_10_2024.gguf",
-    token=api_token
+    model=HF_REPO,
+    token=HF_TOKEN
 )
 
+# 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)
+
+    # 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
+
+# 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)
 
@@ -40,17 +72,18 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
         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 history with the new message and response
+    history.append((message, cleaned_response))  # Update chat history
     
-    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 friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a helpful AI.", 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)"),
@@ -58,4 +91,4 @@ demo = gr.ChatInterface(
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()