Update app.py

app.py

@@ -1,84 +1,86 @@
-#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."
-)
-
-
+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()