Create app.py

app.py

@@ -0,0 +1,68 @@
+import gradio as gr
+import torch
+import torch.nn as nn
+import torchvision.transforms as T
+import torchvision.models as models
+from PIL import Image
+import json
+import os
+
+# -----------------------------
+# Load Class Names
+# -----------------------------
+model_path = "model/model.pth"
+
+checkpoint = torch.load(model_path, map_location="cpu")
+class_names = checkpoint["class_names"]
+
+# -----------------------------
+# Load Model
+# -----------------------------
+model = models.resnet50(pretrained=False)
+model.fc = nn.Linear(model.fc.in_features, len(class_names))
+model.load_state_dict(checkpoint["model_state_dict"], strict=True)
+model.eval()
+
+# -----------------------------
+# Image Preprocessing
+# -----------------------------
+transform = T.Compose([
+    T.Resize((224,224)),
+    T.ToTensor(),
+    T.Normalize([0.485, 0.456, 0.406],
+                [0.229, 0.224, 0.225])
+])
+
+# -----------------------------
+# Prediction Function
+# -----------------------------
+def predict(img):
+    img = transform(img).unsqueeze(0)
+    with torch.no_grad():
+        outputs = model(img)
+        probs = torch.softmax(outputs[0], dim=0)
+
+    # Return top 3 predictions
+    top3_probs, top3_idxs = torch.topk(probs, 3)
+    result = {class_names[i]: float(top3_probs[idx]) 
+              for idx, i in enumerate(top3_idxs)}
+
+    return result
+
+# -----------------------------
+# Gradio Interface
+# -----------------------------
+title = "🐾 Animal Classifier — ResNet50 Fine-Tuned"
+description = """
+Upload an image of an animal and the model will predict what species it is.
+"""
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=3),
+    title=title,
+    description=description,
+)
+
+iface.launch()