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| 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() | |