Delete app.py

app.py

@@ -1,65 +0,0 @@
-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)
-
-# 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()