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README.md

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----
-title: Image Classification
-emoji: 🔥
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.31.0
-app_file: app.py
-pinned: false
-short_description: A Waste Recycle Classification AI Model
+# Smart Waste Classifier ♻️
+
+This project classifies recyclable waste into categories using a fine-tuned MobileNetV2 model with TensorFlow and Gradio.
+
+## How to Use
+
+Upload an image of waste (e.g., plastic bottle, metal can, glass) and the model predicts its category.
+
+## Model Info
+
+- Backbone: MobileNetV2
+- Framework: TensorFlow
+- Interface: Gradio
+- Deployment: Hugging Face Spaces
+
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Upload the included `model.h5` file along with this app to your Hugging Face Space to deploy it live.

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model("model.h5")
+IMG_SIZE = (224, 224)
+
+# Class labels
+index_to_label = {
+    0: "paper", 1: "e-waste", 2: "metal", 3: "light blubs",
+    4: "organic", 5: "plastic", 6: "clothes", 7: "glass", 8: "batteries"
+}
+
+def classify_image(img):
+    img = tf.image.resize(img, IMG_SIZE)
+    img = tf.expand_dims(img, axis=0)
+    img = tf.cast(img, tf.float32) / 255.0
+    pred = model.predict(img)[0]
+    return {index_to_label[i]: float(pred[i]) for i in range(len(pred))}
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="numpy", label="Upload Waste Image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Smart Waste Classifier"
+)
+
+demo.launch()

requirements.txt

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+gradio
+tensorflow
+numpy
+matplotlib