Delete denseNet121.py

denseNet121.py

@@ -1,67 +0,0 @@
-import numpy as np
-import gradio as gr
-import tensorflow as tf
-from PIL import Image, ImageDraw, ImageFont
-
-# Function to load the modified model without recompiling
-def load_modified_model(model_path):
-    return tf.keras.models.load_model(model_path)
-
-# Load the trained model
-print("Loading model...")
-model = load_modified_model('denseNet121.h5')
-print("Model loaded successfully.")
-
-# Function to classify food vs. non-food image using the loaded model
-def classify_food_vs_nonfood(image):
-    try:
-        # Preprocess image
-        image_size = (224, 224)
-        image = image.resize(image_size)
-        image_np = np.array(image) / 255.0
-        image_np_expanded = np.expand_dims(image_np, axis=0)
-
-        # Make prediction
-        prediction = model.predict(image_np_expanded)
-        final_prediction = np.argmax(prediction[0])
-
-        # Display result
-        results = {0: 'Food', 1: 'Non Food'}
-        label = results[final_prediction]
-        
-        # Create a draw object
-        draw = ImageDraw.Draw(image)
-        
-        # Specify font and size
-        font = ImageFont.load_default()
-        
-        # Get text size
-        text_font = ImageFont.truetype("Hack-Regular.ttf", 24)
-        text_bbox = draw.textbbox((0, 0), label, font=text_font)
-        text_size = (text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1])
-        
-        # Calculate text position
-        text_position = ((image_size[0] - text_size[0]) // 2, 10)
-        
-        # Add text to the image
-        draw.text(text_position, label, fill=(255, 0, 0), font=text_font)
-
-        # Return modified image
-        return image
-    except Exception as e:
-        print("Error processing image:", e)
-
-# Define inputs for Gradio interface
-image_input = gr.inputs.Image(shape=(224, 224), type="pil")
-
-# Define example images as file paths
-ex_image_paths = ['image_1.jpeg', 'image_2.jpeg', 'image_3.jpeg', 'image_4.jpg', 'image_5.jpg']
-
-# Launch Gradio interface with example images
-food_vs_nonfood_interface = gr.Interface(classify_food_vs_nonfood, 
-                                         inputs=image_input, 
-                                         outputs="image",
-                                         title="Food vs NonFood Classifier",
-                                         description="Upload an image to classify whether it's food or non-food.",
-                                         examples=ex_image_paths)
-food_vs_nonfood_interface.launch(inline=False)