Update app.py

app.py

@@ -1,16 +1,57 @@
 import gradio as gr
+from keras.models import load_model
+from keras.preprocessing import image
+from keras.applications.vgg16 import preprocess_input
+import numpy as np
+import cv2
+from PIL import Image
+import uuid
 
+# Load your pre-trained model
+model = KerasModel
+
+# Function to preprocess the image for prediction
+def preprocess_image(img_array, s=224):
+    img = (img_array * 255).astype(np.uint8)  # Convert back to uint8
+    img_array = cv2.resize(img, (s, s))
+    img_array = img_array.astype(np.uint8)
+    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    return img_array
+
+# Function to make predictions
 def predict_mineral(img):
-    predicted_class, prediction_score, class_info = predict_mineral(img)
-    return f"Predicted mineral: {predicted_class} ({prediction_score*100:.2f}%)\n\n{class_info}"
-
-demo = gr.Interface(
-    fn=predict_mineral,
-    inputs=gr.Image(shape=(224, 224)),
-    outputs="text",
-    title="Mineral Identification",
-    description="Upload an image of a mineral and I'll try to identify it!",
-    article="This app uses a pre-trained model to predict the type of mineral in an image."
-)
-
-demo.launch(share=True))
+    # Convert the PIL Image to a NumPy array
+    img_array = np.array(img)
+    # Generate a unique filename for the temporary image
+    img_path = f"temp_image_{uuid.uuid4()}.jpg"
+    Image.fromarray((img_array * 255).astype(np.uint8)).save(img_path)
+    # Preprocess the image and make predictions
+    processed_img = preprocess_image(img_array)
+    prediction = model.predict(processed_img)
+    class_idx = np.argmax(prediction)
+    # Update this with your class labels
+    class_labels = ['biotite', 'granite', 'olivine', 'plagioclase', 'staurolite']
+    print(f"Prediction scores: {prediction[0]}")
+    print(f"Predicted class: {class_labels[class_idx]}")
+    # Convert prediction scores to percentages
+    prediction_scores = [f"{score*100:.2f}%" for score in prediction[0]]
+    # Create a formatted string to display the class labels and prediction scores
+    class_info = "\n".join([f"{label}: {score}" for label, score in zip(class_labels, prediction_scores)])
+    return class_labels[class_idx], float(prediction[0][class_idx]), class_info
+
+# Load CSS and JavaScript files
+
+
+# Create a Gradio interface
+with gr.Blocks() as iface:
+    with gr.Row():
+        image_input = gr.Image(label="Input Image")
+        output_text = gr.Textbox(label="Predicted Class")
+        output_number = gr.Number(label="Prediction Score")
+    with gr.Row():
+        class_output = gr.Textbox(label="Class Predictions")
+        run_button = gr.Button("Predict")
+
+    run_button.click(predict_mineral, inputs=image_input, outputs=[output_text, output_number, class_output])
+
+iface.launch(share=True)