Create app.py

app.py

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+import streamlit as st
+import random
+from transformers import pipeline
+
+# Load the GPT2 model
+generator = pipeline('text-generation', model='gpt2')
+
+# Set the maximum length of the generated prompt
+max_length = 50
+
+# Define the prompts and solutions
+prompts = {
+    "Difficulty sleeping": [
+        "Try keeping a consistent sleep schedule and avoid caffeine before bedtime.",
+        "Make your bedroom a comfortable and calming environment.",
+        "Avoid using electronic devices before bedtime.",
+        "Try relaxation techniques like deep breathing or meditation.",
+        "Consider talking to a healthcare provider if sleep problems persist."
+    ],
+    "Time management": [
+        "Use a planner or time-tracking app to prioritize tasks and stay on schedule.",
+        "Break down large tasks into smaller ones.",
+        "Limit multitasking and focus on one task at a time.",
+        "Delegate tasks to others when possible.",
+        "Take regular breaks and avoid overworking yourself."
+    ],
+    "Stress management": [
+        "Practice mindfulness techniques such as deep breathing or meditation.",
+        "Get regular exercise to reduce stress and improve mood.",
+        "Get enough sleep and practice good sleep habits.",
+        "Take breaks throughout the day to reduce stress levels.",
+        "Try to identify the sources of stress in your life and develop strategies to manage them."
+    ]
+}
+
+# Define the function to generate the prompts and solutions
+def generate_prompt(prompt):
+    # Get a random solution for the prompt
+    solution = random.choice(prompts[prompt])
+    # Generate the prompt text
+    prompt_text = f"What can I do to {prompt.lower()}? "
+    # Generate the prompt output
+    output = generator(prompt_text, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True)
+    # Decode the prompt output
+    output_text = output[0]['generated_text'][len(prompt_text):].strip()
+    # Return the generated prompt and solution
+    return output_text, solution
+
+# Define the streamlit app
+def app():
+    # Set the app title
+    st.title('ICL-LM Interface')
+    # Get the user input
+    option = st.selectbox('Select a problem:', list(prompts.keys()))
+    # Generate the prompt and solution
+    prompt, solution = generate_prompt(option)
+    # Display the prompt
+    st.write('Prompt:', prompt)
+    # Display the solution
+    st.write('Solution:', solution)
+
+# Run the streamlit app
+if __name__ == '__main__':
+    while True:
+        app()