Delete App.py

App.py

@@ -1,53 +0,0 @@
-import tensorflow as tf
-from PIL import Image
-import numpy as np
-import gradio as gr
-from huggingface_hub import hf_hub_download
-
-# Load the trained model
-repo_id = "methestrikerx100/Mineral_Identifcation_Project"
-filename = "Deployment.h5"
-
-# Download the model file
-model_path = hf_hub_download(repo_id=repo_id, filename=filename)
-
-# Load the model
-model = tf.keras.models.load_model(model_path)
-
-# Define the class labels
-class_labels = ['biotite', 'granite', 'olivine', 'plagioclase', 'staurolite']
-
-# Define the function to make predictions
-def classify_image(image):
-    # Preprocess the image
-    image = np.array(image)
-    image = Image.fromarray(image.astype(np.uint8), 'RGB')
-    image = image.resize((224, 224))
-    image = np.array(image) / 255.0
-    image = np.expand_dims(image, axis=0)
-
-    # Make prediction
-    prediction = model.predict(image)
-    class_idx = np.argmax(prediction)
-    prediction_scores = prediction[0]
-
-    # Convert prediction scores to percentages
-    prediction_scores_percentages = [f"{score * 100:.2f}%" for score in prediction_scores]
-
-    # Create formatted output strings
-    predicted_class_name = class_labels[class_idx]
-    predicted_scores = "\n".join([f"{label}: {score}" for label, score in zip(class_labels, prediction_scores_percentages)])
-
-    return predicted_class_name, predicted_scores
-
-# Create the Gradio interface
-with gr.Blocks() as demo:
-    with gr.Row():
-        image_input = gr.Image(elem_id="image_input", type="pil")
-        output_components = [
-            gr.Textbox(elem_id="predicted_class_name"),
-            gr.Textbox(elem_id="predicted_scores", lines=5)
-        ]
-        image_button = gr.Button("Classify Image")
-        image_button.click(classify_image, inputs=image_input, outputs=output_components)
-demo.launch(share=True)