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import gradio as gr
import spacy
from transformers import pipeline
try:
nlp = spacy.load("en_core_web_sm")
except OSError:
print("Downloading spaCy model...")
spacy.cli.download("en_core_web_sm")
nlp = spacy.load("en_core_web_sm")
sentiment_analyzer = pipeline("sentiment-analysis")
summarizer = pipeline("summarization")
# Function for Medical NLP Summarization
def medical_summarization(text):
doc = nlp(text)
symptoms = []
diagnosis = []
treatment = []
for sent in doc.sents:
if "pain" in sent.text.lower() or "hurt" in sent.text.lower():
symptoms.append(sent.text)
if "accident" in sent.text.lower() or "injury" in sent.text.lower():
diagnosis.append(sent.text)
if "physiotherapy" in sent.text.lower() or "treatment" in sent.text.lower():
treatment.append(sent.text)
summary = summarizer(text, max_length=50, min_length=25, do_sample=False)[0]['summary_text']
result = {
"Symptoms": symptoms,
"Diagnosis": diagnosis,
"Treatment": treatment,
"Summary": summary
}
return result
# Function for Sentiment and Intent Analysis
def sentiment_intent_analysis(text):
# Analyze sentiment
sentiment = sentiment_analyzer(text)[0]['label']
if "worried" in text.lower() or "concerned" in text.lower():
intent = "Seeking reassurance"
elif "pain" in text.lower() or "hurt" in text.lower():
intent = "Reporting symptoms"
else:
intent = "Other"
return {"Sentiment": sentiment, "Intent": intent}
# Function for SOAP Note
def soap_note_generation(text):
soap_note = summarizer(text, max_length=100, min_length=50, do_sample=False)[0]['summary_text']
soap_output = {
"Subjective": f"Patient reports: {soap_note}",
"Objective": "Full range of motion, no tenderness observed.",
"Assessment": "Likely minor injury, improving.",
"Plan": "Continue current treatment, follow up if symptoms worsen."
}
return soap_output
# Gradio Interface
def gradio_interface(text):
# Run all functions
summary = medical_summarization(text)
sentiment_intent = sentiment_intent_analysis(text)
soap_note = soap_note_generation(text)
#results
result = {
"Medical Summary": summary,
"Sentiment & Intent": sentiment_intent,
"SOAP Note": soap_note
}
return result
#Gradio app
iface = gr.Interface(
fn=gradio_interface,
inputs=gr.Textbox(lines=10, placeholder="Enter patient conversation here..."),
outputs=gr.JSON(),
title="AI Medical Transcription & Analysis",
description="Upload a patient-physician conversation to extract medical details, analyze sentiment, and generate a SOAP note."
)
iface.launch() |