Update app.py

app.py

@@ -8,32 +8,42 @@ import gradio as gr
 from joblib import load
 
 
+# Load the trained Random Forest classifier
+rf_classifier = load('random_forest_model2.joblib')
+
+# Get the class labels from the trained Random Forest classifier
+class_labels = rf_classifier.classes_
+
+
 # Load pre-trained ResNet50 model without top layers
 base_model = ResNet50(weights='imagenet', include_top=False)
 
 # Function to extract features using ResNet50
-def extract_resnet_features(img_path):
-    img = image.load_img(img_path, target_size=(224, 224))
+def extract_resnet_features(image_data):
+    # Convert image data to image array
+    img = image.array_to_img(image_data, scale=False)
+    img = img.resize((224, 224))  # Resize image to match ResNet50 input size
     x = image.img_to_array(img)
     x = np.expand_dims(x, axis=0)
     x = preprocess_input(x)
+    
+    # Extract features using ResNet50
     features = base_model.predict(x)
     features_flattened = features.flatten()
     return features_flattened
 
 
-# Load the trained Random Forest classifier
-rf_classifier = load('random_forest_model2.joblib')
-
 # Function to make predictions
 def predict(image):
     # Convert image to feature vector using ResNet50 (you can replace this with your feature extraction method)
     features = extract_resnet_features(image)
     
     # Make prediction using Random Forest classifier
-    prediction = rf_classifier.predict([features])[0]
+    predicted_index = rf_classifier.predict([features_flattened])[0]
     
-    return prediction
+    # Decode predicted class using the class labels obtained from the Random Forest classifier
+    predicted_class = class_labels[predicted_index]
+    return predicted_class
 
 # Define Gradio interface
 iface = gr.Interface(fn=predict, inputs="image", outputs="text", title="Brain Tumor Classification")