app.py

import gradio as gr
import os
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch

print("🚀 Starting GPT-2 Text Generator...")

# Load environment variables
HF_TOKEN = os.getenv("HF_TOKEN")
API_KEY = os.getenv("API_KEY") 
ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD")

print(f"HF_TOKEN: {'Set' if HF_TOKEN else 'Not set'}")
print(f"API_KEY: {'Set' if API_KEY else 'Not set'}")
print(f"ADMIN_PASSWORD: {'Set' if ADMIN_PASSWORD else 'Not set'}")

# Load model and tokenizer
print("Loading GPT-2 model...")
try:
    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
    model = GPT2LMHeadModel.from_pretrained("gpt2")
    tokenizer.pad_token = tokenizer.eos_token
    print("✅ Model loaded successfully!")
except Exception as e:
    print(f"❌ Error loading model: {e}")
    raise e

def generate_text(prompt, max_length=100, temperature=0.7):
    """Simple text generation function"""
    if not prompt:
        return "Please enter a prompt"
    
    if len(prompt) > 500:
        return "Prompt too long (max 500 characters)"
    
    try:
        print(f"Generating text for: {prompt[:30]}...")
        
        # Encode the prompt
        inputs = tokenizer.encode(prompt, return_tensors="pt", max_length=300, truncation=True)
        
        # Generate text
        with torch.no_grad():
            outputs = model.generate(
                inputs,
                max_length=inputs.shape[1] + max_length,
                temperature=temperature,
                do_sample=True,
                pad_token_id=tokenizer.eos_token_id,
                eos_token_id=tokenizer.eos_token_id,
                no_repeat_ngram_size=2
            )
        
        # Decode the output
        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
        
        # Extract only the new text (remove the original prompt)
        new_text = generated_text[len(prompt):].strip()
        
        print(f"✅ Generated {len(new_text)} characters")
        return new_text if new_text else "No text generated. Try a different prompt."
        
    except Exception as e:
        error_msg = f"Error generating text: {str(e)}"
        print(f"❌ {error_msg}")
        return error_msg

# Create the Gradio interface
print("Creating Gradio interface...")

with gr.Blocks() as demo:
    gr.Markdown("# GPT-2 Text Generator")
    gr.Markdown("Enter a prompt and click generate to create text using GPT-2")
    
    with gr.Row():
        with gr.Column():
            prompt_input = gr.Textbox(
                label="Enter your prompt",
                placeholder="Type your text here...",
                lines=3
            )
            
            max_length_slider = gr.Slider(
                minimum=20,
                maximum=200,
                value=100,
                step=10,
                label="Max length of generated text"
            )
            
            temperature_slider = gr.Slider(
                minimum=0.1,
                maximum=1.5,
                value=0.7,
                step=0.1,
                label="Temperature (creativity)"
            )
            
            generate_button = gr.Button("Generate Text", variant="primary")
        
        with gr.Column():
            output_text = gr.Textbox(
                label="Generated Text",
                lines=8,
                placeholder="Generated text will appear here..."
            )
    
    # Add some example prompts
    gr.Examples(
        examples=[
            "Once upon a time",
            "The future of technology is",
            "In a world where",
        ],
        inputs=prompt_input
    )
    
    # Connect the generate function
    generate_button.click(
        fn=generate_text,
        inputs=[prompt_input, max_length_slider, temperature_slider],
        outputs=output_text
    )

# Launch the app
print("Launching Gradio app...")

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

print("✅ App is running!")