import os
import gradio as gr
from transformers import pipeline
# from huggingface_hub import login

# # Get the Hugging Face token from environment variables
# HF_TOKEN = os.getenv('HF')

# if not HF_TOKEN:
#     raise ValueError("The HF environment variable is not set. Please set it to your Hugging Face token.")

# # Authenticate with Hugging Face and save the token to the Git credentials helper
# login(HF_TOKEN, add_to_git_credential=True)

# Create the pipeline for text generation using the specified model
# pipe = pipeline("text-generation", model="distilbert/distilgpt2", token=HF_TOKEN)
pipe = pipeline("text-generation", model="openai-community/gpt2-medium")

# Define the initial prompt for the system
system_prompt = """
You are an AI model designed to provide concise information about big data analytics across various fields without mentioning the question. Respond with a focused, one-line answer that captures the essence of the key risk, benefit, or trend associated with the topic.

input: What do you consider the most significant risk of over-reliance on big data analytics in stock market risk management?
output: Increased market volatility.

input: What is a major benefit of big data analytics in healthcare?
output: Enhanced patient care through personalized treatment.

input: What is a key challenge of big data analytics in retail?
output: Maintaining data privacy and security.

input: What is a primary advantage of big data analytics in manufacturing?
output: Improved production efficiency and predictive maintenance.

input: What is a significant risk associated with big data analytics in education?
output: Potential widening of the achievement gap if data is not used equitably.
"""

def generate(text):
    try:
        # Combine the system prompt with the user's input
        prompt = system_prompt + f"\ninput: {text}\noutput:"

        # Generate the response using the pipeline
        responses = pipe(prompt, max_length=1024, num_return_sequences=1)
        response_text = responses[0]['generated_text'].split("output:")[-1].strip()

        return response_text if response_text else "No valid response generated."
    
    except Exception as e:
        return str(e)

iface = gr.Interface(
    fn=generate,
    inputs=gr.Textbox(lines=2, placeholder="Enter text here..."),
    outputs="text",
    title="Big Data Analytics Assistant",
    description="Provides concise information about big data analytics across various fields.",
    live=False
)

def launch_custom_interface():
    iface.launch()

if __name__ == "__main__":
    launch_custom_interface()