Update main.py

main.py

@@ -2,14 +2,15 @@ from flask import Flask, request, jsonify
 from flask_cors import CORS
 from PIL import Image, ImageDraw
 import io
-import base64,time
+import base64
+import time
 
 from model import predict
 
 app = Flask(__name__)
 CORS(app)
 
-#global variables
+# global variables
 threshold = 0.02
 diagnosis = ''
 pc_source = False
@@ -24,10 +25,9 @@ def process_annotation():
             source = data.get('source', 'pc') if data else 'pc'
 
         if source == "pc":
-
             global pc_source
             pc_source = True
-            
+
             if not data:
                 return jsonify({"error": "No JSON data provided"}), 400
             img_data = data.get('image')
@@ -35,7 +35,6 @@ def process_annotation():
             category = data.get('category')
             view = data.get('view')
 
-            #Decode base64 image
             header, encoded = img_data.split(',', 1)
             img_bytes = base64.b64decode(encoded)
             img = Image.open(io.BytesIO(img_bytes))
@@ -46,21 +45,22 @@ def process_annotation():
                 bbox['x'] + bbox['width'],
                 bbox['y'] + bbox['height']
             ))
+
             print(f"\nParameters:[Category-{category}, View-{view}, Annotations-{bbox}]\n")
             print("Processing...\n")
             time.sleep(2)
+
             error = predict(cropped, view, category)
             label = get_label(error)
             confidence = 100 - (100 * float(error))
             diagnosis = build_diagnosis(error, confidence)
-            
+
             draw = ImageDraw.Draw(img)
             draw.rectangle(
                 [bbox['x'], bbox['y'], bbox['x'] + bbox['width'], bbox['y'] + bbox['height']],
                 outline="red", width=3
             )
 
-            # Re-encode processed images to base64
             buffered = io.BytesIO()
             cropped.save(buffered, format="PNG")
             processed_b64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
@@ -68,7 +68,7 @@ def process_annotation():
 
             print(f"Results: Error-{error}, Comment-{label}, Confidence-{confidence}, Diagnosis-{diagnosis}\n")
             print(f"Rendering results to: {source} \n")
-            
+
             return jsonify({
                 "processed_image": processed_data_url,
                 "category": category,
@@ -84,31 +84,28 @@ def process_annotation():
             view = request.form.get('view')
             source_field = request.form.get('source')
             image_file = request.files.get('image')
-            
+
             if not image_file:
                 return jsonify({"error": "No image file provided"}), 400
-                
+
             if not category or not view:
                 return jsonify({"error": "Missing category or view"}), 400
 
             try:
                 img = Image.open(image_file)
-                
                 if img.mode != 'RGB':
                     img = img.convert('RGB')
-                    
             except Exception as e:
                 return jsonify({"error": f"Invalid image file: {str(e)}"}), 400
 
-            print(f"\nParameters:[Category-{category}, View-{view}, Image size-{img.size}\n")
+            print(f"\nParameters:[Category-{category}, View-{view}, Image size-{img.size}]\n")
             print("Processing...\n")
-            
+
             error = predict(img, view, category)
             label = get_label(error)
             confidence = 100 - (100 * float(error))
             diagnosis = build_diagnosis(error, confidence)
-            
-            # Convert image back to base64 for response
+
             buffered = io.BytesIO()
             img.save(buffered, format="PNG")
             processed_b64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
@@ -128,8 +125,9 @@ def process_annotation():
 
             print(f"Results: Error-{error}, Comment-{label}, Confidence-{confidence}, Diagnosis-{diagnosis}\n")
             print(f"Rendering results to: {source} \n")
-            
+
             return jsonify(response_data)
+
         else:
             return jsonify({"error": "Unsupported source"}), 400
 
@@ -138,26 +136,19 @@ def process_annotation():
         return jsonify({"error": str(e)}), 500
 
 def get_label(error):
-    label = "⚠️ Anomaly detected" if error > threshold else "✅ Normal structure"
-    return label
+    return "⚠️ Anomaly detected" if error > threshold else "✅ Normal structure"
 
-#RUBRIC      
 def build_diagnosis(error, confidence):
     if confidence < 100 and confidence > 98 and error > threshold:
         diagnosis = 'High probability that there is an anomaly in the structure.'
     elif confidence < 98 and confidence > 96 and error > threshold:
         diagnosis = 'My analysis concludes there could be an anomaly in the structure, but either the structure is not well annotated or the image could be distorted, small, unclear or bad hence the uncertainty.'
     elif confidence < 96 and confidence > 92 and error > threshold:
-        diagnosis = 'High possibility that anomaly is false psoitive and image is greatly distorted or irrelevant. Please check the image or the annotation and try again. If results are same, consult an expert.'
+        diagnosis = 'High possibility that anomaly is false positive and image is greatly distorted or irrelevant. Please check the image or the annotation and try again. If results are same, consult an expert.'
     elif confidence < 92 and error > threshold:
         diagnosis = 'Please upload a good ultrasound scan to obtain diagnosis. I cannot recognize the image nor the outlined structure.'
     elif confidence < 100 and confidence > 98 and error < threshold:
         diagnosis = 'Healthy structure detected. Annotation is correct OR model partially detects healthy area.'
-    
-    
-    diagnosis += " THIS IS NOT PROFESSIONAL MEDICAL ADVICE. LIAS WITH AN EXPERT"
-    
-    return diagnosis
 
-if __name__ == '__main__':
-    app.run(debug=True, port=5000)
+    diagnosis += " THIS IS NOT PROFESSIONAL MEDICAL ADVICE. LIASE WITH AN EXPERT"
+    return diagnosis