app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from datasets import load_dataset
import random
import os

# Check if fine-tuned model exists, otherwise use base model
model_path = "./customer_support_chatbot" if os.path.exists("./customer_support_chatbot") else "microsoft/DialoGPT-medium"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)

# Load the customer support dataset
dataset = load_dataset("Victorano/customer-support-1k")

def generate_response(message, history):
    # Format the input with conversation history
    conversation = ""
    for user_msg, bot_msg in history:
        conversation += f"Customer: {user_msg}\nSupport: {bot_msg}\n"
    conversation += f"Customer: {message}\nSupport:"
    
    # Encode the conversation
    input_ids = tokenizer.encode(conversation, return_tensors='pt')
    
    # Generate response
    with torch.no_grad():
        output_ids = model.generate(
            input_ids,
            max_length=1000,
            num_return_sequences=1,
            no_repeat_ngram_size=2,
            temperature=0.7,
            top_k=50,
            top_p=0.9,
            pad_token_id=tokenizer.eos_token_id
        )
    
    # Decode and return the response
    response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    # Extract only the last response (after "Support:")
    response = response.split("Support:")[-1].strip()
    return response

# Create the Gradio interface
with gr.Blocks(css="footer {display: none !important}") as demo:
    gr.Markdown("""
    # 🤖 Customer Support Chatbot
    This chatbot is fine-tuned on customer support conversations using DialoGPT-medium.
    """)
    
    chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        bubble_full_width=False,
        avatar_images=(None, "https://api.dicebear.com/7.x/bottts/svg?seed=1"),
        height=500,
        show_copy_button=True,
    )
    
    with gr.Row():
        txt = gr.Textbox(
            show_label=False,
            placeholder="Type your message here...",
            container=False
        )
        submit_btn = gr.Button("Send", variant="primary")
    
    # Handle user input and generate response
    def user_input(message, history):
        return "", history + [[message, generate_response(message, history)]]
    
    # Connect the interface components
    txt.submit(user_input, [txt, chatbot], [txt, chatbot])
    submit_btn.click(user_input, [txt, chatbot], [txt, chatbot])

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