import tensorflow as tf from PIL import Image import numpy as np import cv2 import gradio as gr from tensorflow import keras from keras.models import load_model import folium from Map import * from tensorflow.keras.models import load_model loaded_feature_extractor = load_model("feature_extractor_model_5_8") # Load the SVM model import pickle with open("svm_model_probablity_5_8.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") # 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.", 'granite': "Hardness: 6-7\nMagnetism: None\nDensity: 2.6-2.7 g/cm³\nColors: Gray, pink, white\nDescription: An intrusive igneous rock composed mainly of quartz, feldspar, and mica.", 'olivine': "Hardness: 6.5-7\nMagnetism: None\nDensity: 3.2-3.4 g/cm³\nColors: Green, yellow-green, brown\nDescription: A nesosilicate mineral with a green, glassy appearance, commonly found in mafic and ultramafic rocks.", '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 img = (img_array * 255).astype(np.uint8) # Convert back to uint8 img_array = cv2.resize(img, (150, 150)) # Resize to 150x150 img_array = img_array.astype(np.uint8) 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: 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 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 = 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 else: # 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 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.", "", "", "" # 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] # Get probabilities for all classes probabilities = loaded_svm_model.predict_proba(image_features)[0] # 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)]) # Get key facts about the predicted mineral mineral_key_facts = mineral_facts.get(predicted_class_name, "No key facts available for this mineral.") 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 = '''

Microscopic Mineral Identification App

Welcome to our interactive space dedicated to identifying minerals through microscopic imagery. This platform showcases various microscopic images that reveal the intricate patterns and characteristics of different minerals. To get started, you can explore our collection of mineral images or use your own to identify key features such as crystal structure, color variations, and inclusions.

🔎 For a deeper understanding of mineral identification techniques and how to analyze microscopic mineral images, visit our comprehensive mineral guide. It provides insights into common mineralogical features and how to recognize them.

🧪 Interested in more advanced mineralogy? Check Mindat which can provide more details about the mineral identified Mindat.org section, where we dive into more complex mineral structures and analytical methods.

''' # Welcome Message 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; } """ with gr.Blocks( title=app_title, css=custom_css, theme=gr.themes.Monochrome(), ) as demo: gr.Markdown(DESCRIPTION) # 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), ] image_button = gr.Button("Classify Mineral") image_button.click( classify_image, inputs=image_input, outputs=output_components ) demo.launch(auth_message="Welcome to the Mineral Identification App.")