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app.py

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+import streamlit as st
+from transformers import pipeline
+from huggingface_hub import login
+import torch
+import os
+
+# Set page configuration
+st.set_page_config(page_title="Text GenAI Model", page_icon="🤖")
+st.title("Text GenAI Model")
+st.subheader("Answer Random Questions Using Hugging Face Models")
+
+# Fetch Hugging Face token from Streamlit Secrets
+access_token_read = st.secrets["HUGGINGFACE_TOKEN"]  # Ensure this is set in your Streamlit Cloud Secrets
+
+# Free up GPU memory (if using GPU)
+torch.cuda.empty_cache()
+
+# Set environment variable to avoid fragmentation
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
+
+# Login to Hugging Face Hub using the access token
+login(token=access_token_read)
+
+# Initialize the text generation pipeline with GPT-2 model
+pipe = pipeline("text-generation", model="gpt2", device=-1)  # Using CPU
+
+# Input from the user
+text = st.text_input("Ask a Random Question")
+
+if text:
+    # Generate text based on the random question
+    response = pipe(f"Answer the question: {text}", max_length=150, num_return_sequences=1)
+    
+    # Display the generated response
+    st.write(f"Answer: {response[0]['generated_text']}")

requirements.txt

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+streamlit
+transformers
+torch
+huggingface_hub