app.py

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

# 1️⃣ Load the model
MODEL_REPO = "DSDUDEd/firebase"  # your HF model repo
tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO)
model = AutoModelForCausalLM.from_pretrained(MODEL_REPO)

# Set device (CPU or GPU if available)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# 2️⃣ Chat history
chat_history = []

# 3️⃣ Function to generate AI response
def chat_with_ai(user_input):
    global chat_history
    chat_history.append(f"You: {user_input}")
    
    # Prepare input for the model
    input_text = "\n".join(chat_history) + "\nAI:"
    inputs = tokenizer(input_text, return_tensors="pt").to(device)
    
    # Generate output
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7,
        top_p=0.9,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Extract only the AI's last response
    ai_response = response.split("AI:")[-1].strip()
    chat_history.append(f"AI: {ai_response}")
    
    # Display the chat nicely
    return "\n".join(chat_history)

# 4️⃣ Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## 🤖 Custom GPT-2 AI Chat")
    chatbot = gr.Textbox(label="Your Message", placeholder="Type here...", lines=2)
    output = gr.Textbox(label="Chat Output", interactive=False, lines=15)
    send_button = gr.Button("Send")

    send_button.click(fn=chat_with_ai, inputs=chatbot, outputs=output)

# 5️⃣ Launch the Space
demo.launch()