import gradio as gr
from transformers import pipeline
import os

# Initialize Hugging Face model
model_name = "gpt2"  # Use an appropriate Hugging Face model (e.g., GPT-2 for general text generation)
generator = pipeline("text-generation", model=model_name)

# Function to generate the construction checklist using Hugging Face model
def generate_checklist(project_type, project_size, location, timeline):
    prompt = f"Generate a detailed construction checklist for a {project_type} with {project_size} in {location}. The project timeline is {timeline}."
    
    # Generate text using Hugging Face model
    generated_text = generator(prompt, max_length=500, num_return_sequences=1)
    return generated_text[0]['generated_text'].strip()

# Gradio interface
def gradio_interface():
    # Inputs for the form
    inputs = [
        gr.Textbox(label="Project Type (e.g., House, Office, Shed)", placeholder="Enter the project type..."),
        gr.Textbox(label="Project Size (e.g., 2000 sq ft)", placeholder="Enter the project size..."),
        gr.Textbox(label="Location (optional, e.g., California)", placeholder="Enter location..."),
        gr.Textbox(label="Timeline (optional, e.g., 6 months)", placeholder="Enter the project timeline..."),
    ]
    
    # Output for the generated checklist
    output = gr.Textbox(label="Generated Construction Checklist")
    
    # Create and launch the Gradio interface
    gr.Interface(fn=generate_checklist, inputs=inputs, outputs=output, live=True).launch()

# Run the Gradio interface when the script is executed
if __name__ == "__main__":
    gradio_interface()