Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# Load the GPT2 tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = GPT2LMHeadModel.from_pretrained('gpt2')
+
+# Set the maximum length of the generated prompt
+max_length = 50
+
+# Define the prompts
+prompts = [
+    "Difficulty sleeping: ",
+    "Time management: ",
+    "Stress management: ",
+    "Healthy eating: ",
+    "Exercise: ",
+    "Financial planning: ",
+    "Communication skills: ",
+    "Career development: ",
+    "Relationship issues: ",
+    "Self-improvement: "
+]
+
+# Define the solutions
+solutions = [
+    "Try keeping a consistent sleep schedule and avoid caffeine before bedtime.",
+    "Use a planner or time-tracking app to prioritize tasks and stay on schedule.",
+    "Practice mindfulness techniques such as deep breathing or meditation.",
+    "Incorporate more fruits and vegetables into your diet and limit processed foods.",
+    "Aim for at least 30 minutes of moderate physical activity daily.",
+    "Create a budget and track expenses to avoid overspending.",
+    "Practice active listening and express yourself clearly and assertively.",
+    "Set clear goals and seek feedback and professional development opportunities.",
+    "Practice empathy and active communication with your partner or seek professional counseling.",
+    "Read self-help books, learn new skills or hobbies, and practice self-reflection."
+]
+
+# Define the function to generate the prompts
+def generate_prompt(prompt):
+    # Generate the prompt text
+    prompt_text = prompt + tokenizer.eos_token
+    # Encode the prompt text
+    encoded_prompt = tokenizer.encode(prompt_text, return_tensors='pt')
+    # Generate the prompt output
+    output = model.generate(encoded_prompt, max_length=max_length, num_return_sequences=1, no_repeat_ngram_size=2, early_stopping=True)
+    # Decode the prompt output
+    output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+    # Return the generated prompt
+    return output_text
+
+# Define the streamlit app
+def app():
+    # Set the app title
+    st.title('Prompt Generator')
+    # Get the user input
+    option = st.selectbox('Select a prompt:', prompts)
+    # Generate the prompt
+    prompt = generate_prompt(option)
+    # Display the prompt
+    st.write('Prompt:', option + prompt)
+    # Display the solution
+    st.write('Solution:', solutions[prompts.index(option)])
+
+# Run the streamlit app
+if __name__ == '__main__':
+    app()