Create app.py

app.py

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+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import streamlit as st
+import json
+
+# Load the fine-tuned model and tokenizer
+model_name = "."
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+
+# Function to generate text based on input
+def generate_text(input_text):
+    # Tokenize and generate text with sampling and different decoding parameters
+    input_ids = tokenizer.encode(input_text, return_tensors="pt", max_length=512)
+    generated_text = model.generate(
+        input_ids,
+        max_length=200,
+        num_beams=5,
+        temperature=0.9,  # Adjust the temperature for more randomness
+        no_repeat_ngram_size=2,
+        top_k=50,
+        top_p=0.95,
+        early_stopping=True,
+        do_sample=True,
+    )
+
+    # Decode and return the generated text
+    decoded_text = tokenizer.decode(generated_text[0], skip_special_tokens=True)
+    return decoded_text
+
+# Streamlit app
+def main():
+    
+     # Apply custom styling for the title
+    st.markdown("<h3 style='text-align: center; color: #333;'>Medical Summary - Text Generation</h3>", unsafe_allow_html=True)
+
+    # Textbox for user input
+    user_input = st.text_area("Enter Text:", "")
+
+    # Button to trigger text generation
+    if st.button("Compute"):
+        if user_input:
+            # Call the generate_text function with user input
+            result = generate_text(user_input)
+
+            # Display the generated text in a box with word wrap
+            #st.markdown(f"**Generated Text:**\n\n```\n{result}\n```", unsafe_allow_html=True)
+            #st.text(result)
+            # Display the generated text in a div with word wrap and auto-increasing height
+            #st.markdown(f"<div style='white-space: pre-wrap; overflow-y: auto;'>**Generated Text:**\n\n```\n{result}\n```</div>", unsafe_allow_html=True)
+            # Display the generated text in a div with word wrap and auto-increasing width and height
+            #st.markdown(f"<div style='white-space: pre-wrap; width: 100%; overflow: auto;'>**Generated Text:**\n\n```\n{result}\n```</div>", unsafe_allow_html=True)
+            # Display the generated text in a text area with word wrap
+            st.text_area("Generated Text:", result, key="generated_text")
+
+            
+            
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    main()