added demo model for garbage classification

.gitignore

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+flagged/
+*.png
+*.jpg
+*.mp4
+*.jpeg
+*.mkv
+.DS_Store
+gradio_cached_examples/
+venv/
+.locks/
+models-*

app.py

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+from ast import mod
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+
+# Load the pre-trained model from Hugging Face
+model_name = "iamsuman/household-waste-EfficientNetV2M"
+
+model_path = hf_hub_download(repo_id=model_name, filename="EfficientNetV2M.h5")
+
+
+model = tf.keras.models.load_model(model_path)
+
+
+# Define a manual mapping of label indices to human-readable labels
+index_to_label = {
+    0: "battery",
+    1: "biological",
+    2: "cardboard",
+    3: "clothes",
+    4: "glass",
+    5: "metal",
+    6: "paper",
+    7: "plastic",
+    8: "shoes",
+    9: "trash"
+}
+
+def classify_image(image):
+
+    image = Image.fromarray(image.astype('uint8'), 'RGB')
+
+    image = image.resize((400, 400))
+    # Convert the PIL Image to a format compatible with the model
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    image = tf.keras.applications.efficientnet.preprocess_input(image)
+
+    # Make prediction
+    preds = model.predict(image)
+
+    # Retrieve the highest probability class label index
+    predicted_class_idx = np.argmax(preds, axis=-1)[0]
+
+    # Convert the index to the model's class label
+    label = index_to_label.get(predicted_class_idx, "Unknown Label")
+
+    return label.capitalize()
+
+# Create Gradio interface
+iface = gr.Interface(fn=classify_image, 
+                     inputs=gr.Image(),  # Accepts image of any size
+                     outputs=gr.Label(),
+                     title="Household Waste Classification with EfficientNetV2M",
+                     description="Upload an image of household waste, and the model will classify it.")
+
+# Launch the app
+iface.launch()

requirements.txt

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+# Base ----------------------------------------
+huggingface_hub>=0.23.4
+numpy>=1.18.5
+torch>=1.7.0
+Pillow>=7.1.2
+
+# Logging -------------------------------------
+tensorflow==2.15