Update app.py

app.py

@@ -6,26 +6,25 @@ import gradio as gr
 from tensorflow import keras
 from keras.models import load_model
 import folium
-import re
 
+from Map import *
 
 
 
 
-
-
-
-# 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_probablity.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")
+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']
@@ -36,6 +35,11 @@ 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:
@@ -46,6 +50,7 @@ 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
 def preprocess_image_classification(img_array):
     if img_array is None:
@@ -56,6 +61,7 @@ def preprocess_image_classification(img_array):
     img_array = np.expand_dims(img_array, axis=0)  # Add 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:
@@ -74,17 +80,23 @@ def detect_mineral(image):
         # Handle the case where no image is provided
         return "No image provided."
 
+
 # Define the function to make predictions
 def classify_image(image):
+    if image is None:
+        # Handle the case where no image is provided
+        return "No image provided.", "", "", ""
+
     # 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 placeholders for non-mineral input
+        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.", "", ""
+        return "Error preprocessing image.", "", "", ""
 
     # Extract features using the loaded CNN feature extractor
     image_features = loaded_feature_extractor.predict(image)
@@ -100,18 +112,20 @@ def classify_image(image):
     # Convert prediction scores to percentages
     prediction_scores_percentages = [f"{score * 100:.2f}%" for score in probabilities]
 
-    predicted_scores = "\n".join([f"{label}: {score}" for label, score in zip(class_labels, prediction_scores_percentages)])
+    predicted_scores = "\n".join(
+        [f"{label}: {score}" for label, score in zip(class_labels, prediction_scores_percentages)])
 
     # Get key facts about the predicted mineral
     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
-
-
-
-
-
+    if predicted_class_name:
+        # Generate the mineral map
+        mineral_map = generate_mineral_map(predicted_class_name)
+        mineral_map_html = mineral_map._repr_html_()
+    else:
+        mineral_map_html = ""
 
+    return predicted_class_name, predicted_scores, mineral_key_facts, mineral_map_html
 DESCRIPTION = '''
 <div>
 <h1 style="text-align: center;">Microscopic Mineral Identification App</h1>
@@ -127,40 +141,29 @@ def welcome(name):
     return f"Welcome to Gradio, {name}!"
 
 
-
-
 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;background-color: #f0f0f0;}
-
-
 #title-container {
     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 */
     height: 50px;
 }
-
 """
-
-
-# Gradio Blocks interface
 with gr.Blocks(
-    title=app_title,
-    css=custom_css,
-    theme=gr.themes.Monochrome(),
-  
+        title=app_title,
+        css=custom_css,
+        theme=gr.themes.Monochrome(),
 ) as demo:
     gr.Markdown(DESCRIPTION)
 
@@ -179,9 +182,14 @@ with gr.Blocks(
             outputs=output_components
         )
 
-    gr.Examples(
-        examples=["Gradio examples/Biotite1.jpg", "Gradio examples/Biotite2.jpg", "Gradio examples/Olivine1.jpg", "Gradio examples/Plagioclase1.jpg"],
+    # Add a new row for the map
+    with gr.Row():
+        mineral_map = gr.HTML(label="Mineral Locations Map", elem_id="mineral_map")
+
+    image_button.click(
+        classify_image,
         inputs=image_input,
+        outputs=output_components + [mineral_map]
     )
 
     demo.launch(auth_message="Welcome to the Mineral Identification App.")