Update app.py

app.py

@@ -6,14 +6,19 @@ import gradio as gr
 from tensorflow import keras
 from keras.models import load_model
 
-# Load the classification model
-mineral_classification_model = "Hugging_face_model_final.h5"
-mineral_classification_model = tf.keras.models.load_model(mineral_classification_model)
+# Load the CNN feature extractor model
+from tensorflow.keras.models import load_model
+loaded_feature_extractor = load_model("feature_extractor_model")
+
+# Load the SVM model
+import pickle
+with open("svm_model.pkl", 'rb') as file:
+    loaded_svm_model = pickle.load(file)
 
 # Load the mineral detection model
 mineral_detection_model = tf.keras.models.load_model("mineral_detection_model_Final_4_18_2024.h5")
 
-#  the class labels
+# Define the class labels
 class_labels = ['biotite', 'granite', 'olivine', 'plagioclase', 'staurolite']
 mineral_facts = {
     'biotite': "Hardness: 2.5-3\nMagnetism: None\nDensity: 2.7-3.3 g/cm³\nColors: Black, brown, green\nDescription: A phyllosilicate mineral of the mica group with a distinctive platy structure.",
@@ -22,8 +27,7 @@ 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
+# Function to preprocess the image for mineral detection
 def preprocess_image_detection(img_array):
     if img_array is None:
         return None
@@ -33,7 +37,7 @@ def preprocess_image_detection(img_array):
     img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
     return img_array
 
-# function to preprocess the image for classification
+# Function to preprocess the image for classification
 def preprocess_image_classification(img_array):
     if img_array is None:
         return None
@@ -43,46 +47,60 @@ def preprocess_image_classification(img_array):
     img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
     return img_array
 
-#   function to detect if the input is a mineral
+# 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 = image.resize((150, 150))  # Resize to 150x150
+        image = np.array(image)
+        image = Image.fromarray(image.astype(np.uint8), 'RGB')
+        image = image.resize((150, 150))  # Assuming the model expects 150x150 images
         image = np.array(image) / 255.0
-        image = np.expand_dims(image, axis=0)  # Add batch dimension
-        
+        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
     else:
+        # Handle the case where no image is provided
         return "No image provided."
 
-#  function to make predictions for mineral classification
+# Define the function to make predictions
 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 mineral thin section.", "", ""
+        return "Input is not a Microscopic mineral thin section, Please Insert a thin section.", "", ""
     
     # Preprocess the image for classification
     image = preprocess_image_classification(np.array(image))
     if image is None:
         return "Error preprocessing image.", "", ""
     
-    # Make prediction
-    prediction = mineral_classification_model.predict(image)
-    class_idx = np.argmax(prediction)
-    
-    prediction_scores = prediction[0]
-    prediction_scores_percentages = [f"{score * 100:.2f}%" for score in prediction_scores]
+    # Extract features using the loaded CNN feature extractor
+    image_features = loaded_feature_extractor.predict(image)
     
+    # Make prediction using the loaded SVM model
+    predicted_label = loaded_svm_model.predict(image_features)
+    class_idx = predicted_label[0]
     predicted_class_name = class_labels[class_idx]
-    predicted_scores = "\n".join([f"{label}: {score}" for label, score in zip(class_labels, prediction_scores_percentages)])
+    
+    # Get prediction scores for all classes
+    decision_values = loaded_svm_model.decision_function(image_features)
+    prediction_scores = [f"{label}: {score:.4f}" for label, score in zip(class_labels, decision_values[0])]
+    predicted_scores = "\n".join(prediction_scores)
+    
+    # Get the mineral key facts
     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>
@@ -92,11 +110,11 @@ DESCRIPTION = '''
 </div>
 '''
 
-# Welcome Message function
+
+# Welcome Message
 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');
@@ -105,6 +123,7 @@ 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){
@@ -113,36 +132,50 @@ 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 */
+    width: 50px; /* Adjust the logo size as needed */
     height: 50px;
 }
+
 """
 
+
 with gr.Blocks(
     title=app_title,
     css=custom_css,
@@ -151,20 +184,25 @@ with gr.Blocks(
 ) as demo:
     gr.Markdown(DESCRIPTION)
     
-    # Create interface components
+    # Your existing code for creating the 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=[r"C:\Users\nanom\OneDrive\Desktop\Gradio UI\pixlr-image-generator-29d31d6c-51c8-48ac-a157-d893fe98dec0.png"],
         inputs=image_input,
-    )
+        
 
-    demo.launch(auth_message="Welcome to the Mineral Identification App.")
+    )
+    demo.launch( auth_message="Welcome To Mineral Identification App.")