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import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Load the base T5 model and tokenizer
model = T5ForConditionalGeneration.from_pretrained('t5-small')
tokenizer = T5Tokenizer.from_pretrained('t5-small')
def generate_clinical_report(input_text):
"""
Generate a clinical report from the input text using the T5 model.
"""
try:
# Prepare input text
input_ids = tokenizer.encode("summarize: " + input_text, return_tensors="pt", max_length=512, truncation=True)
# Generate report
outputs = model.generate(
input_ids,
max_length=256,
num_beams=4,
no_repeat_ngram_size=3,
length_penalty=2.0,
early_stopping=True,
bad_words_ids=[[tokenizer.encode(word, add_special_tokens=False)[0]]
for word in ['http', 'www', '.com', '.org']]
)
# Decode and return the generated report
return tokenizer.decode(outputs[0], skip_special_tokens=True)
except Exception as e:
print(f"Error generating report: {str(e)}")
return f"Error: {str(e)}"
# Create Gradio interface
demo = gr.Interface(
fn=generate_clinical_report,
inputs=gr.Textbox(
lines=8,
placeholder="Enter clinical notes here...",
label="Clinical Notes"
),
outputs=gr.Textbox(
lines=8,
label="Generated Clinical Report"
),
title="Clinical Report Generator",
description="Generate professional clinical reports from clinical notes using a T5 model.",
allow_flagging="never"
)
# Launch the app
if __name__ == "__main__":
demo.queue(concurrency_count=1) # Single request at a time for stability
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
root_path="",
show_api=True,
allowed_paths=None, # Allow all paths
quiet=True # Reduce logging noise
)
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