app.py

import os
import gradio as gr
import random
from transformers import pipeline
from transformers import AutoModelForCausalLM, AutoTokenizer


# Get the Hugging Face token from environment variables
hf_token = os.getenv("gpt2_token")

if not hf_token:
    raise ValueError("Hugging Face token not found. Please set HF_TOKEN as an environment variable.")

model = AutoModelForCausalLM.from_pretrained(
    "isitcoding/gpt2_120_finetuned",
    config="adapter_config.json",  # Specify the custom config file
    state_dict="adapter_model.safetensors"  # Specify the custom model weights
)
tokenizer = AutoTokenizer.from_pretrained("isitcoding/gpt2_120_finetuned")
# Load the text generation pipeline with your fine-tuned model
generator = pipeline('text-generation', model=model, tokenizer=tokenizer, use_auth_token = hf_token)

# Function to generate responses using the text generation model
def respond(message, chat_history):
    # Generate a response from the model
    response = generator(message, max_length=1028, num_return_sequences=3)[0]['generated_text']
    # Append the user message and model response to chat history
    chat_history.append(("User", message))
    chat_history.append(("Bot", response))
    return chat_history

# Create a Gradio interface using Blocks
with gr.Blocks() as demo:
    # Add a Chatbot component
    chatbot = gr.Chatbot()
    # Add a textbox for user input
    msg = gr.Textbox(label="Enter your message")
    # Add a button to clear the chat
    clear = gr.Button("Clear")

    # Define what happens when the user submits a message
    msg.submit(respond, [msg, chatbot], chatbot)
    # Define what happens when the clear button is pressed
    clear.click(lambda: [], None, chatbot)

# Launch the Gradio interface
demo.launch()