File size: 2,226 Bytes
647a796 9e0003a e213acb 647a796 2248522 647a796 2248522 647a796 a344135 2248522 a344135 2248522 a344135 647a796 2248522 647a796 2248522 647a796 52bab4e 2248522 647a796 9e0003a 647a796 9e0003a 1310cf1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import gradio as gr
from transformers import T5Tokenizer, T5ForConditionalGeneration
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
app = FastAPI()
# Configure CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["https://pdarleyjr.github.io"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# 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"
)
# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, demo, path="/")
# Launch the app with FastAPI integration
if __name__ == "__main__":
import uvicorn
demo.queue(max_size=20)
uvicorn.run(
app,
host="0.0.0.0",
port=7860,
log_level="info"
)
|