Upload app.py

app.py

@@ -1,76 +1,82 @@
-from PIL import Image
-import torch
-import torchvision.transforms as transforms
-from torchvision import models
-import gradio as gr
-
-# 🔧 Set device
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-# 📦 Load your fine-tuned model
-model = models.resnet50(pretrained=False)
-model.fc = torch.nn.Linear(model.fc.in_features, 2)  # 2 classes: Edible, Poisonous
-model.load_state_dict(torch.load("resnet_mushroom_classifier.pth", map_location=device))
-model = model.to(device)
-model.eval()
-
-# 🏷️ Class names
-class_names = ['Edible', 'Poisonous']
-
-# 🍄 Mapping for more detailed species
-mushroom_species = {
-    "Edible": "Possible species:\n• Amanita citrina\n• Russula delica\n• Phaeogyroporus portentosus",
-    "Poisonous": "Possible species:\n• Amanita phalloides\n• Inocybe rimosa"
-}
-
-# 🎨 Image preprocessing (must match training)
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.485, 0.456, 0.406],
-                         [0.229, 0.224, 0.225])
-])
-
-# 🧠 Prediction function
-def classify_mushroom(image: Image.Image):
-    try:
-        image = image.convert("RGB")
-        tensor = transform(image).unsqueeze(0).to(device)
-
-        with torch.no_grad():
-            outputs = model(tensor)
-            _, predicted = torch.max(outputs, 1)
-            label = class_names[predicted.item()]
-            score = torch.softmax(outputs, dim=1)[0][predicted.item()].item() * 100
-
-        suggestion = mushroom_species[label]
-
-        return label, "กินได้" if label == "Edible" else "พิษ", f"{score:.2f}%", suggestion
-
-    except Exception as e:
-        print(f"❌ Error: {e}")
-        return "Error", "ผิดพลาด", "N/A", "N/A"
-
-# 🎛️ Gradio UI
-if __name__ == "__main__":
-    with gr.Blocks() as demo:
-        gr.Markdown("## 🍄 Mushroom Safety Classifier")
-        gr.Markdown("Upload a mushroom photo to check if it’s edible or poisonous.\nอัปโหลดรูปเห็ดเพื่อทำนายว่าเห็ดกินได้หรือมีพิษ")
-
-        with gr.Row():
-            image_input = gr.Image(type="pil", label="📷 Upload Mushroom Image")
-            with gr.Column():
-                label_en = gr.Textbox(label="🧠 Prediction (English)")
-                label_th = gr.Textbox(label="🗣️ คำทำนาย (ภาษาไทย)")
-                confidence = gr.Textbox(label="📶 Confidence Score")
-                label_hint = gr.Textbox(label="🏷️ Likely Species (Based on Training Data)")
-
-        classify_btn = gr.Button("🔍 Classify")
-
-        classify_btn.click(
-            fn=classify_mushroom,
-            inputs=image_input,
-            outputs=[label_en, label_th, confidence, label_hint]
-        )
-
-    demo.launch()
+from PIL import Image
+import torch
+import torchvision.transforms as transforms
+from torchvision import models
+import gradio as gr
+from rembg import remove  # 🆕 Background removal
+
+# 🔧 Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# 📦 Load your fine-tuned model
+model = models.resnet50(pretrained=False)
+model.fc = torch.nn.Linear(model.fc.in_features, 2)  # 2 classes: Edible, Poisonous
+model.load_state_dict(torch.load("resnet_mushroom_classifier.pth", map_location=device))
+model = model.to(device)
+model.eval()
+
+# 🏷️ Class names
+class_names = ['Edible', 'Poisonous']
+
+# 🍄 Mapping for more detailed species
+mushroom_species = {
+    "Edible": "Possible species:\n• Amanita citrina\n• Russula delica\n• Phaeogyroporus portentosus",
+    "Poisonous": "Possible species:\n• Amanita phalloides\n• Inocybe rimosa"
+}
+
+# 🎨 Image preprocessing (must match training)
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+# 🧠 Prediction function with background removal
+def classify_mushroom(image: Image.Image):
+    try:
+        image = image.convert("RGB")
+
+        # 🆕 Remove background
+        image = remove(image)  # returns RGBA with transparency
+        image = image.convert("RGB")  # back to RGB
+
+        tensor = transform(image).unsqueeze(0).to(device)
+
+        with torch.no_grad():
+            outputs = model(tensor)
+            _, predicted = torch.max(outputs, 1)
+            label = class_names[predicted.item()]
+            score = torch.softmax(outputs, dim=1)[0][predicted.item()].item() * 100
+
+        suggestion = mushroom_species[label]
+
+        return label, "กินได้" if label == "Edible" else "พิษ", f"{score:.2f}%", suggestion
+
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        return "Error", "ผิดพลาด", "N/A", "N/A"
+
+# 🎛️ Gradio UI
+if __name__ == "__main__":
+    with gr.Blocks() as demo:
+        gr.Markdown("## 🍄 Mushroom Safety Classifier")
+        gr.Markdown("Upload a mushroom photo to check if it’s edible or poisonous.\nอัปโหลดรูปเห็ดเพื่อทำนายว่าเห็ดกินได้หรือมีพิษ")
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="📷 Upload Mushroom Image")
+            with gr.Column():
+                label_en = gr.Textbox(label="🧠 Prediction (English)")
+                label_th = gr.Textbox(label="🗣️ คำทำนาย (ภาษาไทย)")
+                confidence = gr.Textbox(label="📶 Confidence Score")
+                label_hint = gr.Textbox(label="🏷️ Likely Species (Based on Training Data)")
+
+        classify_btn = gr.Button("🔍 Classify")
+
+        classify_btn.click(
+            fn=classify_mushroom,
+            inputs=image_input,
+            outputs=[label_en, label_th, confidence, label_hint]
+        )
+
+    demo.launch()