Add model

app.py

@@ -1,7 +1,65 @@
 import gradio as gr
+import numpy as np
+from PIL import Image
 
-def greet(name):
-    return "Hello " + name + "!!"
+from tensorflow.keras.applications.resnet50 import preprocess_input
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+from datasets import load_dataset
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+ds = load_dataset("dvk65/TrashTypes")
+class_names = ds["train"].features["label"].names
+
+REPO_ID = "dvk65/trash-classifier-resnet50"
+FILENAME = "trashclassify_13.keras"
+
+model_path = hf_hub_download(repo_id=REPO_ID, filename=FILENAME)
+
+model = tf.keras.models.load_model(
+    model_path,
+    custom_objects={"preprocess_input": preprocess_input}
+)
+
+
+# --- PREPROCESSING ---
+def preprocess(image):
+    image = image.resize((224, 224))           # depends on your model
+    image = np.array(image) / 255.0            # normalize
+    image = np.expand_dims(image, axis=0)      # add batch dimension
+    return image
+
+
+# --- PREDICTION FUNCTION ---
+def predict(img):
+    img = preprocess(img)
+    preds = model.predict(img)[0]              # shape: (num_classes,)
+
+    class_names = [
+        "apples",
+        "bananas",
+        "bottles",
+        "cans",
+        "cardboard",
+        "cups",
+        "eggshells",
+        "generalcompost",     # mixed leftover food
+        "mixers",             # wooden coffee stirrers
+        "peels",              # oranges
+        "platicbags",         # typo in original dataset? keep as-is
+        "plastics",           # plastic wrappers
+        "tissue papers"
+    ]
+
+    result = {class_names[i]: float(preds[i]) for i in range(len(preds))}
+    return result
+
+# --- GRADIO UI ---
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Trash Classifier (ResNet50)",
+    description="Upload an image of trash and get the predicted type."
+)
+
+demo.launch()