app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load model and tokenizer
model_name = "microsoft/DialoGPT-small"  # or any other chat model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# To keep conversation context, store user input in a global
# or external variable. But for simplicity, let's use a simple function.

chat_history = []

def chatbot(input_text):
    # Use global chat_history
    global chat_history

    # Encode the new user input, plus the chat history
    input_ids = tokenizer.encode(tokenizer.eos_token.join(chat_history) + tokenizer.eos_token + input_text + tokenizer.eos_token, return_tensors='pt')
    
    # Generate response
    output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
    response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    
    # Update chat history
    chat_history.append(input_text)
    chat_history.append(response)

    return response

iface = gr.Interface(fn=chatbot, 
                     inputs="text", 
                     outputs="text", 
                     title="AI Girlfriend/Boyfriend Chatbot")

def run_app():
    iface.launch(server_name="0.0.0.0", server_port=7860)

if __name__ == "__main__":
    run_app()