Update app.py

app.py

@@ -5,83 +5,6 @@ from torchvision import transforms
 from torchvision.models import swin_t
 from PIL import Image
 
-# 🔧 Model definition
-class MMIM(nn.Module):
-    def __init__(self, num_classes=4):
-        super(MMIM, self).__init__()
-        print("[INFO] Initializing MMIM model...")
-        self.backbone = swin_t(weights='IMAGENET1K_V1')
-        self.backbone.head = nn.Identity()
-        self.classifier = nn.Sequential(
-            nn.Linear(768, 512),
-            nn.ReLU(),
-            nn.Dropout(0.3),
-            nn.Linear(512, num_classes)
-        )
-
-    def forward(self, x):
-        features = self.backbone(x)
-        print(f"[DEBUG] Feature shape: {features.shape}")
-        return self.classifier(features)
-
-# ✅ Load model
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-print(f"[INFO] Using device: {device}")
-
-model = MMIM(num_classes=4)
-model.load_state_dict(torch.load("MMIM_best3.pth", map_location=device))
-model.to(device)
-model.eval()
-print("[INFO] Model loaded successfully.")
-
-# ✅ Class names
-class_names = []
-
-# 🔁 Transform
-transform = transforms.Compose([
-    transforms.Resize((224, 224)),
-    transforms.ToTensor()
-])
-
-# 🔍 Prediction
-def predict(img):
-    print("[INFO] Image received for prediction.")
-    img = img.convert('RGB')
-    img_tensor = transform(img).unsqueeze(0).to(device)
-    print(f"[DEBUG] Tensor shape: {img_tensor.shape}")
-
-    with torch.no_grad():
-        outputs = model(img_tensor)
-        probs = torch.softmax(outputs, dim=1)
-        conf, pred = torch.max(probs, 1)
-
-    predicted_class = class_names[pred.item()]
-    confidence = conf.item() * 100
-    print(f"[INFO] Predicted: {predicted_class}, Confidence: {confidence:.2f}%")
-
-    return f"✅ Predicted as a weed with class-{predicted_class}\nConfidence: {confidence:.2f}%"
-
-# 🎨 Gradio Interface
-interface = gr.Interface(
-    fn=predict,
-    inputs=gr.Image(type="pil", label="Upload Weed Image"),  # ✅ tool removed
-    outputs="text",
-    title="Weed Image Classifier",
-    description="Upload a weed image to predict its class. If the model detects a non-weed image, it will return 'Negative'.",
-    allow_flagging="manual",
-    live=True
-)
-
-interface.launch()
-
-
-import gradio as gr
-import torch
-import torch.nn as nn
-from torchvision import transforms
-from torchvision.models import swin_t
-from PIL import Image
-
 # 🔧 Model definition
 class MMIM(nn.Module):
     def __init__(self, num_classes=9):