Create app.py

app.py

@@ -0,0 +1,40 @@
+from tensorflow.keras.applications import ResNet50
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.resnet50 import preprocess_input
+import numpy as np
+
+from sklearn.ensemble import RandomForestClassifier
+import gradio as gr
+from joblib import load
+
+
+# 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))
+    x = image.img_to_array(img)
+    x = np.expand_dims(x, axis=0)
+    x = preprocess_input(x)
+    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]
+    
+    return prediction
+
+# Define Gradio interface
+iface = gr.Interface(fn=predict, inputs="image", outputs="text", title="Brain Tumor Classification")
+iface.launch()