Upload denseNet121.py

denseNet121.py

@@ -0,0 +1,67 @@
+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)