Update app.py

app.py

@@ -1,89 +1,89 @@
-from flask import Flask, request, jsonify, render_template
-import tensorflow as tf
-from keras.models import load_model
-from keras.preprocessing import image
-import numpy as np
-import os
-from werkzeug.utils import secure_filename
-
-app = Flask(__name__)
-
-# Configuration
-UPLOAD_FOLDER = 'static/uploads'
-ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
-app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
-
-# Create upload folder if it doesn't exist
-os.makedirs(UPLOAD_FOLDER, exist_ok=True)
-
-# Load model once at startup
-model = None
-
-def get_model():
-    global model
-    if model is None:
-        model = load_model('brain_tumor_model.h5')
-    return model
-
-def allowed_file(filename):
-    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
-
-def preprocess_image(img_path, target_size=(150, 150)):  # ← Update this!
-    """Preprocess image for model prediction"""
-    img = image.load_img(img_path, target_size=target_size)
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0)
-    img_array = img_array / 255.0  # Normalize
-    return img_array
-
-@app.route("/")
-def index():
-    return render_template('index.html')
-
-@app.route("/predict", methods=["POST"])
-def predict():
-    if 'file' not in request.files:
-        return jsonify({"error": "No file uploaded"}), 400
-    
-    file = request.files['file']
-    
-    if file.filename == '':
-        return jsonify({"error": "No file selected"}), 400
-    
-    if file and allowed_file(file.filename):
-        filename = secure_filename(file.filename)
-        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-        file.save(filepath)
-        
-        try:
-            # Preprocess and predict
-            model = get_model()
-            processed_image = preprocess_image(filepath)
-            prediction = model.predict(processed_image)
-            
-            # Adjust this based on your model's output
-            # For binary classification:
-            if prediction[0][0] > 0.5:
-                result = "Tumor Detected"
-                confidence = float(prediction[0][0]) * 100
-            else:
-                result = "No Tumor Detected"
-                confidence = (1 - float(prediction[0][0])) * 100
-            
-            return jsonify({
-                "success": True,
-                "prediction": result,
-                "confidence": f"{confidence:.2f}%",
-                "image_path": filepath
-            })
-            
-        except Exception as e:
-            return jsonify({"error": str(e)}), 500
-    
-    return jsonify({"error": "Invalid file type. Use PNG, JPG, or JPEG"}), 400
-
-if __name__ == '__main__':
-    print("Loading model...")
-    get_model()
-    print("Model loaded successfully!")
-    app.run(debug=True)
+from flask import Flask, request, jsonify, render_template
+import tensorflow as tf
+from keras.models import load_model
+from keras.preprocessing import image
+import numpy as np
+import os
+from werkzeug.utils import secure_filename
+
+app = Flask(__name__)
+
+# Configuration
+UPLOAD_FOLDER = 'static/uploads'
+ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# Create upload folder if it doesn't exist
+os.makedirs(UPLOAD_FOLDER, exist_ok=True)
+
+# Load model once at startup
+model = None
+
+def get_model():
+    global model
+    if model is None:
+        model = load_model('brain_tumor_model.h5')
+    return model
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def preprocess_image(img_path, target_size=(150, 150)):  # ← Update this!
+    """Preprocess image for model prediction"""
+    img = image.load_img(img_path, target_size=target_size)
+    img_array = image.img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0  # Normalize
+    return img_array
+
+@app.route("/")
+def index():
+    return render_template('index.html')
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file uploaded"}), 400
+    
+    file = request.files['file']
+    
+    if file.filename == '':
+        return jsonify({"error": "No file selected"}), 400
+    
+    if file and allowed_file(file.filename):
+        filename = secure_filename(file.filename)
+        filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(filepath)
+        
+        try:
+            # Preprocess and predict
+            model = get_model()
+            processed_image = preprocess_image(filepath)
+            prediction = model.predict(processed_image)
+            
+            # Adjust this based on your model's output
+            # For binary classification:
+            if prediction[0][0] > 0.5:
+                result = "Tumor Detected"
+                confidence = float(prediction[0][0]) * 100
+            else:
+                result = "No Tumor Detected"
+                confidence = (1 - float(prediction[0][0])) * 100
+            
+            return jsonify({
+                "success": True,
+                "prediction": result,
+                "confidence": f"{confidence:.2f}%",
+                "image_path": filepath
+            })
+            
+        except Exception as e:
+            return jsonify({"error": str(e)}), 500
+    
+    return jsonify({"error": "Invalid file type. Use PNG, JPG, or JPEG"}), 400
+
+if __name__ == '__main__':
+    print("Loading model...")
+    get_model()
+    print("Model loaded successfully!")
+    app.run(host="0.0.0.0",port=7860,debug=True)