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app.py

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+# --- SECTION 1: INSTALLS & IMPORTS ---
+# Run !pip install -q gradio transformers in a separate cell if not already installed
+from transformers import MobileViTImageProcessor, MobileViTForImageClassification
+from PIL import Image
+import requests
+import torch
+import torch.nn.functional as F
+import gradio as gr
+
+# --- SECTION 2: MODEL LOADING ---
+# We load the model once at the top to save memory and time
+model_name = "apple/mobilevit-small"
+processor = MobileViTImageProcessor.from_pretrained(model_name)
+model = MobileViTForImageClassification.from_pretrained(model_name)
+
+# --- SECTION 3: THE CORE LOGIC ---
+def predict_with_confidence(image_input):
+    """
+    Takes a URL or a local file path, processes it through MobileViT,
+    and returns a human-readable label with a confidence score.
+    """
+    try:
+        # 1. Handle Input (URL vs Local)
+        if str(image_input).startswith('http'):
+            image = Image.open(requests.get(image_input, stream=True).raw).convert("RGB")
+        else:
+            image = Image.open(image_input).convert("RGB")
+        
+        # 2. Pre-process the image
+        inputs = processor(images=image, return_tensors="pt")
+        
+        # 3. Run Inference
+        with torch.no_grad():
+            outputs = model(**inputs)
+        
+        # 4. Calculate Results
+        logits = outputs.logits
+        probs = F.softmax(logits, dim=-1)
+        
+        predicted_class_idx = logits.argmax(-1).item()
+        label = model.config.id2label[predicted_class_idx]
+        confidence = probs[0][predicted_class_idx].item()
+        
+        return f"Prediction: {label} | Confidence: {confidence:.2%}"
+    
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# --- SECTION 4: TESTING (CONSOLE) ---
+print("--- RUNNING TESTS ---")
+url_test = "http://images.cocodataset.org/val2017/000000039769.jpg"
+print(f"URL Test: {predict_with_confidence(url_test)}")
+
+# To test your local image, uncomment the line below and paste your path:
+# my_local_path = "/kaggle/input/test-image/my_picture.png"
+# print(f"Local Test: {predict_with_confidence(my_local_path)}")
+
+# --- SECTION 5: INTERACTIVE INTERFACE ---
+# This creates a web UI inside your notebook
+demo = gr.Interface(
+    fn=predict_with_confidence, 
+    inputs=gr.Image(type="filepath", label="Upload Image or Drag & Drop"),
+    outputs=gr.Textbox(label="Model Output"),
+    title="MobileViT Edge-AI Classifier",
+    description="A lightweight Computer Vision model designed for mobile efficiency."
+)
+
+# Set share=True to get a public link you can send to recruiters
+demo.launch(share=True)

requirements.txt

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+transformers
+torch
+gradio
+pillow
+requests