Upload app.py with huggingface_hub

app.py

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+import gradio as gr
+import torch
+from torchvision import models, transforms
+from PIL import Image
+from huggingface_hub import hf_hub_download
+from datasets import load_dataset
+import numpy as np
+
+
+# === Repozytorium z modelem i artefaktami ===
+REPO_ID = "vGiacomov/image-classifier-beans"
+MODEL_FILENAME = "resnet18_beans.pth"
+
+
+# === Automatyczne pobranie modelu z Model Hub ===
+model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
+
+
+# === Wczytanie modelu ===
+model = models.resnet18()
+model.fc = torch.nn.Linear(model.fc.in_features, 3)
+model.load_state_dict(torch.load(model_path, map_location="cpu"))
+model.eval()
+
+
+# === Klasy ===
+labels = ["Healthy", "Bean Rust", "Angular Leaf Spot"]
+
+
+# === Transformacje obrazu ===
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+
+# === Funkcja predykcji ===
+def classify(image):
+    if image is None:
+        return {"No image uploaded": 1.0}
+    try:
+        image = Image.fromarray(image.astype("uint8"), "RGB")
+        tensor = transform(image).unsqueeze(0)
+        with torch.no_grad():
+            outputs = model(tensor)
+            probs = torch.nn.functional.softmax(outputs[0], dim=0)
+        return {labels[i]: float(probs[i]) for i in range(3)}
+    except Exception as e:
+        return {f"Error: {str(e)}": 1.0}
+
+
+# === NOWE: Pobierz przykładowe obrazy z datasetu beans ===
+def get_example_images():
+    """Pobiera przykładowe obrazy z każdej klasy datasetu beans"""
+    try:
+        dataset = load_dataset("beans", split="train")
+        examples = []
+        
+        # Pobierz po jednym przykładzie z każdej klasy (0, 1, 2)
+        for label_id in range(3):
+            # Znajdź pierwszy obraz dla danej klasy
+            for item in dataset:
+                if item["labels"] == label_id:
+                    # Konwertuj PIL Image na numpy array (format wymagany przez Gradio)
+                    img_array = np.array(item["image"])
+                    examples.append(img_array)
+                    break
+        
+        return examples
+    except Exception as e:
+        print(f"Nie udało się załadować przykładów: {e}")
+        return []
+
+
+# === Pobierz przykłady ===
+example_images = get_example_images()
+
+
+# === Interfejs Gradio ===
+gr.Interface(
+    fn=classify,
+    inputs=gr.Image(type="numpy", sources=["upload"], label="Upload an image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Bean Disease Classifier",
+    description="Upload an image of a bean leaf to detect disease.",
+    examples=example_images if example_images else None,
+    cache_examples=False  # Unikaj cachowania na CPU Basic
+).launch(debug=True)