Update app.py

app.py

@@ -5,18 +5,45 @@ import gradio as gr
 from tensorflow import keras
 from keras.models import load_model
 
-model_path = "VGG_16.h5"
-model = load_model(model_path, compile=False)
-
-
+# Load the classification model
+model = "B7_MODEL_Final.h5"
+model = tf.keras.models.load_model(model)
 
+# Load the mineral detection model mineral_detection_model_Final_4_18_2024.h5
+mineral_detection_model = tf.keras.models.load_model("mineral_detection_model_Final_4_18_2024.h5")
 
 # Define the class labels
 class_labels = ['biotite', 'granite', 'olivine', 'plagioclase', 'staurolite']
 
+# Define the function to detect if the input is a mineral
+def detect_mineral(image):
+    # Preprocess the image for mineral detection model
+    if image is not None:
+        image = Image.fromarray(np.array(image).astype(np.uint8), 'RGB')
+        # Rest of your code for detecting minerals
+    else:
+        # Handle the case where no image is provided
+        return "No image provided."
+    image = np.array(image)
+    image = Image.fromarray(image.astype(np.uint8), 'RGB')
+    image = image.resize((150, 150))  # Assuming the model expects 224x224 images
+    image = np.array(image) / 255.0
+    image = np.expand_dims(image, axis=0)
+
+    # Make prediction
+    prediction = mineral_detection_model.predict(image)
+    is_mineral = prediction[0][0] < 0.5  # Assuming binary classification
+
+    return is_mineral
+
 # Define the function to make predictions
 def classify_image(image):
-    # Preprocess the image
+    # Check if the input is a mineral
+    is_mineral = detect_mineral(image)
+    if not is_mineral:
+        return "Input is not a mineral.", ""
+
+    # Preprocess the image for classification
     image = np.array(image)
     image = Image.fromarray(image.astype(np.uint8), 'RGB')
     image = image.resize((224, 224))
@@ -38,15 +65,13 @@ def classify_image(image):
     return predicted_class_name, predicted_scores
 
 # Create the Gradio interface
-iface = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Textbox(label="Predicted Class"),
-        gr.Textbox(label="Prediction Scores", lines=5),
-    ],
-    title="Mineral Identification App",
-)
-
-# Launch the app
-iface.launch()
+with gr.Blocks() as demo:
+    with gr.Row():
+        image_input = gr.Image(elem_id="image_input", type="pil")
+        output_components = [
+            gr.Textbox(elem_id="predicted_class_name"),
+            gr.Textbox(elem_id="predicted_scores", lines=5)
+        ]
+        image_button = gr.Button("Classify Image")
+        image_button.click(classify_image, inputs=image_input, outputs=output_components)
+    demo.launch(auth=("admin", "pass1234"), share=True, auth_message="Welcome To Mineral Identification App, Please login to use the app.")