add model

.gitignore

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+.env

app.py

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+import gradio as gr
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import requests
+import os
+
+# Ensure model folder exists
+os.makedirs("model", exist_ok=True)
+
+# Download the model from Hugging Face if not already present
+model_path = "model/mobnet_model.keras"
+if not os.path.exists(model_path):
+    url = "https://huggingface.co/ahmzakif/TrashNet-Classification/resolve/main/model/mobnet_model.keras"
+    r = requests.get(url)
+    with open(model_path, "wb") as f:
+        f.write(r.content)
+
+# Load Keras model
+model = tf.keras.models.load_model(model_path)
+
+# TrashNet classes
+classes = ["cardboard", "glass", "metal", "paper", "plastic", "trash"]
+
+# Image preprocessing
+def predict(image: Image.Image):
+    image = image.convert("RGB").resize((224, 224))
+    x = np.array(image, dtype=np.float32) / 255.0
+    x = np.expand_dims(x, axis=0)
+    
+    preds = model.predict(x)[0]
+    scores = {classes[i]: float(preds[i]) for i in range(len(classes))}
+    top_class = max(scores, key=scores.get)
+    
+    return {"prediction": top_class, "scores": scores}
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="TrashNet Classification API",
+    description="Upload an image of trash to get its classification."
+)
+
+iface.launch()

requirements.txt

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+gradio
+tqdm==4.66.5
+imutils==0.5.4
+numpy==1.26.4
+pandas==2.0.3
+pillow==10.4.0
+matplotlib==3.7.3
+seaborn==0.11.0
+albumentations==1.4.1
+opencv-python==4.10.0.84
+tensorflow==2.15.1
+keras==2.15.1
+scikit-learn==1.2.2
+wandb==0.19.1