Update app.py

app.py

@@ -7,8 +7,8 @@ from tensorflow import keras
 from keras.models import load_model
 
 # Load the classification model
-model = "Hugging_face_model_final.h5"
-model = tf.keras.models.load_model(model)
+mineral_classification_model = "Hugging_face_model_final.h5"
+mineral_classification_model = tf.keras.models.load_model(mineral_classification_model)
 
 # Load the mineral detection model
 mineral_detection_model = tf.keras.models.load_model("mineral_detection_model_Final_4_18_2024.h5")
@@ -22,6 +22,7 @@ mineral_facts = {
     'plagioclase': "Hardness: 6-6.5\nMagnetism: None\nDensity: 2.6-2.8 g/cm³\nColors: White, gray, green\nDescription: A series of feldspar minerals ranging from sodium-rich albite to calcium-rich anorthite.",
     'staurolite': "Hardness: 7-7.5\nMagnetism: None\nDensity: 3.6-3.8 g/cm³\nColors: Brown, reddish-brown, black\nDescription: A nesosilicate mineral with a distinctive cruciform twinning habit, commonly found in metamorphic rocks."
 }
+
 # Function to preprocess the image for mineral detection
 def preprocess_image_detection(img_array):
     if img_array is None:
@@ -38,34 +39,30 @@ def preprocess_image_classification(img_array):
         return None
     img = (img_array * 255).astype(np.uint8)  # Convert back to uint8
     img_array = cv2.resize(img, (224, 224))  # Resize to 224x224
-    img_array = img_array.astype(np.uint8)
-    img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+    img_array is expected to be uint8 and with batch dimension
     return img_array
 
 # Define the function to detect if the input is a mineral
 def detect_mineral(image):
     if image is not None:
         image = Image.fromarray(np.array(image).astype(np.uint8), 'RGB')
-        image = np.array(image)
-        image = Image.fromarray(image.astype(np.uint8), 'RGB')
-        image = image.resize((150, 150))  # Assuming the model expects 150x150 images
+        image = image.resize((150, 150))  # Resize to 150x150
         image = np.array(image) / 255.0
-        image = np.expand_dims(image, axis=0)
-
+        image = np.expand_dims(image, axis=0)  # Add batch dimension
+        
         # Make prediction
         prediction = mineral_detection_model.predict(image)
         is_mineral = prediction[0][0] < 0.5  # Assuming binary classification
         return is_mineral
     else:
-        # Handle the case where no image is provided
         return "No image provided."
 
-# Define the function to make predictions
+# Define the function to make predictions for mineral classification
 def classify_image(image):
     # Check if the input is a mineral
     is_mineral = detect_mineral(image)
     if not is_mineral:
-        return "Input is not a Microscopic mineral thin section, Please Insert a thin section.", "", ""
+        return "Input is not a Microscopic mineral thin section. Please insert a mineral thin section.", "", ""
     
     # Preprocess the image for classification
     image = preprocess_image_classification(np.array(image))
@@ -73,26 +70,18 @@ def classify_image(image):
         return "Error preprocessing image.", "", ""
     
     # Make prediction
-    prediction = model.predict(image)
+    prediction = mineral_classification_model.predict(image)
     class_idx = np.argmax(prediction)
-    prediction_scores = prediction[0]
     
-    # Convert prediction scores to percentages
+    prediction_scores = prediction[0]
     prediction_scores_percentages = [f"{score * 100:.2f}%" for score in prediction_scores]
     
-    # Get the predicted class name and key facts
     predicted_class_name = class_labels[class_idx]
     predicted_scores = "\n".join([f"{label}: {score}" for label, score in zip(class_labels, prediction_scores_percentages)])
     mineral_key_facts = mineral_facts.get(predicted_class_name, "No key facts available for this mineral.")
     
     return predicted_class_name, predicted_scores, mineral_key_facts
 
-
-
-
-
-
-
 DESCRIPTION = '''
 <div>
 <h1 style="text-align: center;">Microscopic Mineral Identification App</h1>
@@ -102,11 +91,11 @@ DESCRIPTION = '''
 </div>
 '''
 
-
-# Welcome Message
+# Welcome Message function
 def welcome(name):
     return f"Welcome to Gradio, {name}!"
 
+# Custom JavaScript for an animation when opening the app
 js = """
 function createGradioAnimation() {
     var container = document.createElement('div');
@@ -115,7 +104,6 @@ function createGradioAnimation() {
     container.style.fontWeight = 'bold';
     container.style.textAlign = 'center';
     container.style.marginBottom = '20px';
-
     var text = 'Welcome to Gradio!';
     for (var i = 0; i < text.length; i++) {
         (function(i){
@@ -124,50 +112,36 @@ function createGradioAnimation() {
                 letter.style.opacity = '0';
                 letter.style.transition = 'opacity 0.5s';
                 letter.innerText = text[i];
-
                 container.appendChild(letter);
-
                 setTimeout(function() {
                     letter.style.opacity = '1';
                 }, 50);
             }, i * 250);
         })(i);
     }
-
     var gradioContainer = document.querySelector('.gradio-container');
     gradioContainer.insertBefore(container, gradioContainer.firstChild);
-
     return 'Animation created';
 }
 """
 
-
 app_title = "Mineral Identification using AI"
 app_description = "This application uses advanced machine learning models to accurately identify and classify different types of minerals from images. Simply upload an image, and the system will provide the predicted mineral class along with its key characteristics and properties."
 
 custom_css = """
 .gradio-container {display: flex; justify-content: center; align-items: center; height: 100vh;}
-
-
 #title-container {
-    display: flex;
-    align-items: center;
-    justify-content: center;
-    margin-bottom: 20px;
+    display: flex; align-items: center; justify-content: center; margin-bottom: 20px;
 }
-
 #app-title {
     margin-right: 20px; /* Adjust the spacing between the title and logo */
 }
-
 #logo-img {
-    width: 50px; /* Adjust the logo size as needed */
+    width: 50px; /* Adjust the logo size */
     height: 50px;
 }
-
 """
 
-
 with gr.Blocks(
     title=app_title,
     css=custom_css,
@@ -176,25 +150,20 @@ with gr.Blocks(
 ) as demo:
     gr.Markdown(DESCRIPTION)
     
-    # Your existing code for creating the interface components
+    # Create interface components
     with gr.Row():
         image_input = gr.Image(elem_id="image_input", type="pil")
         output_components = [
             gr.Textbox(label="Mineral Name", elem_id="predicted_class_name"),
             gr.Textbox(label="Prediction Scores of Model", elem_id="predicted_scores", lines=5),
-            gr.Textbox(label="Key Facts About Mineral", elem_id="mineral_key_facts", lines=8)
+            gr.Textbox(label="Key Facts About Mineral", elem_id="mineral_key_facts", lines=8),
         ]
         image_button = gr.Button("Classify Mineral")
         image_button.click(classify_image, inputs=image_input, outputs=output_components)
 
-    with gr.Row():
-        gr.Textbox(label="Mineral Detection", elem_id="mineral_detection_output", lines=3)
-        gr.Textbox(label="Mineral Classification", elem_id="mineral_classification_output", lines=3)
-        gr.Textbox(label="Mineral Key Facts", elem_id="mineral_key_facts_output", lines=8)
     gr.Examples(
-        examples=["Gradio examples/Biotite1.jpg","Gradio examples/Biotite2.jpg","Gradio examples/Olivine1.jpg","Gradio examples/Plagioclase1.jpg"],
+        examples=["Gradio examples/Biotite1.jpg", "Gradio examples/Biotite2.jpg", "Gradio examples/Olivine1.jpg", "Gradio examples/Plagioclase1.jpg"],
         inputs=image_input,
-        
-
     )
-    demo.launch( auth_message="Welcome To Mineral Identification App.")
+
+    demo.launch(auth_message="Welcome to the Mineral Identification App.")