Create app.py

app.py

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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()