uploaded app.py and txt

App.py.py

@@ -0,0 +1,84 @@
+#import libraries
+import gradio as gr
+import spacy
+from transformers import pipeline
+
+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):
+    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
+
+
+def gradio_interface(text):
+    # Run functions
+    summary = medical_summarization(text)
+    sentiment_intent = sentiment_intent_analysis(text)
+    soap_note = soap_note_generation(text)
+    
+    # result
+    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()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+transformers
+spacy