app.py

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

# Load model
model_name = "microsoft/DialoGPT-medium"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


# Chat function
def reply(message, history):
    if not message.strip():
        return "Please enter a message."

    chat_history_ids = None

    # Handle previous conversation
    for msg in history:
        content = msg["content"]

        # Fix: if content is list → convert to string
        if isinstance(content, list):
            content = " ".join([str(x) for x in content])

        if not isinstance(content, str):
            continue

        ids = tokenizer.encode(content + tokenizer.eos_token, return_tensors="pt")

        if chat_history_ids is None:
            chat_history_ids = ids
        else:
            chat_history_ids = torch.cat([chat_history_ids, ids], dim=-1)

    # Current message
    new_input_ids = tokenizer.encode(message + tokenizer.eos_token, return_tensors="pt")

    if chat_history_ids is not None:
        input_ids = torch.cat([chat_history_ids, new_input_ids], dim=-1)
    else:
        input_ids = new_input_ids

    # Attention mask fix
    attention_mask = torch.ones_like(input_ids)

    # Generate response
    output_ids = model.generate(
        input_ids,
        attention_mask=attention_mask,
        max_length=1000,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        top_k=50,
        top_p=0.95,
        temperature=0.7,
        repetition_penalty=1.2
    )

    # Extract only new response
    response_ids = output_ids[:, input_ids.shape[-1]:]
    response = tokenizer.decode(response_ids[0], skip_special_tokens=True)

    # Fallback
    if response.strip() == "":
        response = "I'm here! How can I help you?"

    return response


# UI
demo = gr.ChatInterface(
    fn=reply,
    title="💬 Smart Dialogue System",
    description="Full conversation chatbot using DialoGPT"
)

# Launch
demo.launch(
    server_name="0.0.0.0",
    server_port=7860,
    ssr_mode=False
)