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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 & history
user_preferences = defaultdict(int) # Tracks keywords & topics
session_histories = defaultdict(list) # Stores conversation history per session
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
# Add past conversation to message history
messages = [{"role": "system", "content": system_message}]
for user_msg, bot_response in session_history:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_response})
# Append current user message
messages.append({"role": "user", "content": message})
# Generate response from model
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response # Stream response to user
# Save to session history
session_history.append((message, response))
# Optionally, adapt responses based on learned preferences
most_asked = max(user_preferences, key=user_preferences.get, default=None)
if most_asked and most_asked in message.lower():
response += f"\n\nI see you're interested in {most_asked} a lot! Want to explore more details?"
yield response # Update response with learning behavior
# Create Chat Interface
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly chatbot that learns user interests.", 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()
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