Update app.py

app.py

@@ -6,16 +6,12 @@ import gradio as gr
 from tensorflow import keras
 from keras.models import load_model
 
-
-
-
-#Back_End_code
 # Load the classification model
-model = r"C:\\Users\\nanom\\OneDrive\\Desktop\\midterm implementation\\Hugging_face_model_final.h5"
+model = "Hugging_face_model_final.h5"
 model = tf.keras.models.load_model(model)
 
 # Load the mineral detection model
-mineral_detection_model = tf.keras.models.load_model(r"C:\\Users\\nanom\\OneDrive\\Desktop\\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']
@@ -26,7 +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
+# Function to preprocess the image for mineral detection
 def preprocess_image_detection(img_array):
     if img_array is None:
         return None
@@ -36,17 +32,17 @@ 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
-    img = (img_array * 255).astype(np.uint8)  
-    img_array = cv2.resize(img, (224, 224))  # resize to 224x224
+    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)
+    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')
@@ -64,24 +60,24 @@ def detect_mineral(image):
         # Handle the case where no image is provided
         return "No image provided."
 
-# function to make predictions
+# 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 thin section.", "", ""
     
-    # preprocess the image for classification
+    # Preprocess the image for classification
     image = preprocess_image_classification(np.array(image))
     if image is None:
         return "Error preprocessing image.", "", ""
     
-    #  prediction
+    # Make prediction
     prediction = model.predict(image)
     class_idx = np.argmax(prediction)
     prediction_scores = prediction[0]
     
-    # convert prediction scores to percentages
+    # Convert prediction scores to percentages
     prediction_scores_percentages = [f"{score * 100:.2f}%" for score in prediction_scores]
     
     # Get the predicted class name and key facts
@@ -91,12 +87,23 @@ def classify_image(image):
     
     return predicted_class_name, predicted_scores, mineral_key_facts
 
-
-#Front_End_code
 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."
 
-with gr.Blocks(title=app_title, css=".gradio-container {display: flex; justify-content: center; align-items: center; height: 100vh;}",theme=gr.themes.Monochrome()) as demo:
+with gr.Blocks(
+    title=app_title, 
+    css=".gradio-container {display: flex; justify-content: center; align-items: center; height: 100vh;}",
+    theme=gr.themes.Monochrome(),
+
+    
+    
+    
+    
+    
+    
+    
+    ) as demo:
+    # Your existing code for creating the interface components
     with gr.Row():
         image_input = gr.Image(elem_id="image_input", type="pil")
         output_components = [
@@ -106,4 +113,4 @@ with gr.Blocks(title=app_title, css=".gradio-container {display: flex; justify-c
         ]
         image_button = gr.Button("Classify Mineral")
         image_button.click(classify_image, inputs=image_input, outputs=output_components)
-    demo.launch(share=True)
+    demo.launch(auth=("admin", "pass1234"),auth_message="Welcome To Mineral Identification App.")