app.py

import gradio as gr
from huggingface_hub import InferenceClient
from collections import defaultdict

# Initialize the 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

    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]

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

    # Extract keywords & update preferences
    keywords = extract_keywords(message)
    for kw in keywords:
        user_preferences[kw] += 1

    # 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)
    response = client.text_generation(
        formatted_input,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
    )

    # ✅ Extract only the response
    cleaned_response = response.split("### Response:")[-1].strip()

    # Save to session 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

# Create Chat Interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="Wewe ni msaidizi wa kirafiki anayejifunza upendeleo wa mtumiaji.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, 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)"),
    ],
)

if __name__ == "__main__":
    demo.launch()