Update app.py

app.py

@@ -2,66 +2,48 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 from collections import defaultdict
 
-# Initialize the model client
+# Initialize model client
 client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
 
-# Store user preferences & chat history
-user_preferences = defaultdict(int)  # Tracks user interests
-session_histories = defaultdict(list)  # Stores chat history per session
-
-def format_chat_history(history, system_message):
-    """Formats history into a single string in Alpaca/LLaMA style."""
-    chat_str = f"{system_message}\n\n"  # Start with system message
+# Memory storage
+session_histories = defaultdict(list)  # Stores full chat history per session
 
+def format_chat_history(history):
+    """Formats history in a structured way for LLaMA models."""
+    chat_str = ""  
     for user_msg, bot_response in history:
-        chat_str += f"### Instruction:\n{user_msg}\n\n### Response:\n{bot_response}\n\n"
-
-    return chat_str  # Return formatted conversation history
-
-def extract_keywords(text):
-    """Extracts simple keywords from user input."""
-    words = text.lower().split()
-    common_words = {"the", "is", "a", "and", "to", "of", "in", "it", "you", "for"}  # Ignore common words
-    return [word for word in words if word not in common_words]
+        chat_str += f"User: {user_msg}\nAI: {bot_response}\n"
+    return chat_str.strip()  # Remove unnecessary spaces
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    session_id = id(history)  # Unique ID for each session
-    session_history = session_histories[session_id]  # Retrieve session history
+    session_id = id(history)  # Unique session ID
+    session_history = session_histories[session_id]  # Retrieve stored history
 
-    # Extract keywords & update preferences
-    keywords = extract_keywords(message)
-    for kw in keywords:
-        user_preferences[kw] += 1
+    # Add user message to history
+    formatted_history = format_chat_history(session_history)
+    full_input = f"{system_message}\n\n{formatted_history}\nUser: {message}\nAI:"
 
-    # Format full conversation as a single string
-    formatted_input = format_chat_history(session_history, system_message) + f"### Instruction:\n{message}\n\n### Response:\n"
-
-    # Send request (fix: ensure input is a single string)
+    # Generate response
     response = client.text_generation(
-        formatted_input,
+        full_input,
         max_new_tokens=max_tokens,
         temperature=temperature,
         top_p=top_p,
     )
 
-    # ✅ Extract only the response
-    cleaned_response = response.split("### Response:")[-1].strip()
+    # Extract & clean response
+    cleaned_response = response.strip()
 
-    # Save to session history
+    # Update chat history
     session_history.append((message, cleaned_response))
 
-    # Adapt response based on learning
-    most_asked = max(user_preferences, key=user_preferences.get, default=None)
-    if most_asked and most_asked in message.lower():
-        cleaned_response += f"\n\nNaona unapenda mada ya '{most_asked}' sana! Unataka kujua zaidi?"
-
-    return cleaned_response  # ✅ Fixed: Returns only the final response
+    return cleaned_response
 
-# Create Chat Interface
+# Gradio Chat Interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="Wewe ni msaidizi wa kirafiki anayejifunza upendeleo wa mtumiaji.", label="System message"),
+        gr.Textbox(value="You are an AI assistant that remembers previous conversations.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=250, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),