initial

app.py

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+import gradio as gr
+import os
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+from torchvision.models import ResNet18_Weights
+from PIL import Image
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# 1. Class names (order must match your training)
+class_names = ['Cabbage', 'Carrot', 'Cucumber', 'Tomato']
+
+# 2. Load default preprocessing from pretrained weights
+weights = ResNet18_Weights.DEFAULT
+transform = weights.transforms()
+
+# 3. Define and load the model
+def get_resnet18():
+    model = models.resnet18(weights=weights)
+    num_ftrs = model.fc.in_features
+    model.fc = nn.Linear(num_ftrs, len(class_names))
+    return model
+
+model = get_resnet18()
+model.load_state_dict(torch.load("resnet_model.pth", map_location=torch.device("cpu"), weights_only=False))
+model.eval()
+
+# 4. Prediction function
+def predict(img: Image.Image) -> Tuple[Dict[str, float], float]:
+    start = timer()
+    img_tensor = transform(img).unsqueeze(0)
+
+    with torch.inference_mode():
+        output = model(img_tensor)
+        probs = torch.softmax(output, dim=1)[0]
+
+    result = {class_names[i]: float(probs[i]) for i in range(len(class_names))}
+    elapsed = round(timer() - start, 5)
+    return result, elapsed
+
+# 5. Build the Gradio interface
+with gr.Blocks() as demo:
+    gr.HTML("""
+        <div style="background-image: url('https://images.unsplash.com/photo-1501004318641-b39e6451bec6');
+                    background-size: cover; 
+                    padding: 25px;
+                    border-radius: 10px;
+                    color: white;
+                    font-family: 'Poppins', sans-serif;">
+            <h1 style='text-align: center;'>🌾 Vegetable Classifier with ResNet18 🌾</h1>
+            <h3 style='text-align: center;'>
+                Created by <a href='https://github.com/hassan1303' target='_blank' style='color: #f0e68c;'>@hassan1303</a>
+            </h3>
+        </div>
+    """)
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="📸 Upload Vegetable Image")
+            predict_button = gr.Button("🔍 Predict")
+
+            
+            examples = []
+            if os.path.exists("examples"):
+                examples = [["examples/" + fname] for fname in os.listdir("examples") if fname.lower().endswith((".png", ".jpg", ".jpeg"))]
+            gr.Examples(examples=examples, inputs=[image_input])
+
+        with gr.Column():
+            output_label = gr.Label(num_top_classes=4, label="🌽 Predictions")
+            output_time = gr.Number(label="⏱️ Prediction Time (seconds)")
+
+    predict_button.click(fn=predict, inputs=image_input, outputs=[output_label, output_time])
+
+# 6. Launch the app
+demo.launch()   
+
+
+

requirements.txt

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+torch
+torchvision
+gradio
+Pillow
+

resnet_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8d9092b51f1fde37fa95bcd2e3a20b469bcf003999e5fe2cc46cf5433b4f9be1
+size 44791988