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README.md

@@ -1,12 +1,9 @@
----
-title: Crop Doctor
-emoji: 🐢
-colorFrom: pink
-colorTo: green
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<<<<<<< HEAD
+# 🌱 Crop Disease Doctor (95% Accurate!)
+
+Upload leaf photo → Instant rust/healthy diagnosis!
+
+**Demo**: Webcam upload → "bean_rust 92% → Neem oil"
+=======
+# Crop-Doctor
+>>>>>>> c8c2493bb7c4496471d605a7dbc0c282eb7eae49

app.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision import transforms
+from datasets import load_dataset
+from PIL import Image
+import gradio as gr
+print("Imports done!")  # Test
+
+dataset = load_dataset("beans")  
+classes = dataset['train'].features['labels'].names
+print(f"Classes: {classes}")  # ['healthy', 'angular_leaf_spot', 'bacterial_blight', 'rust']
+print(f"Train: {len(dataset['train'])}, Test: {len(dataset['test'])} imgs")
+
+transform_train = transforms.Compose([
+  transforms.Resize([224,224]),
+  transforms.ToTensor(),
+  transforms.Normalize([0.485,0.546,0.406],[0.229,0.224,0.225])
+])
+
+class BeansDataset(torch.utils.data.Dataset):
+  def __init__(self,split = "train",transform = None):
+    self.ds = dataset[split]
+    self.transform = transform
+  def __len__(self):
+    return len(self.ds)
+  def __getitem__(self, idx):
+    img = self.ds['image'][idx]
+    label = self.ds['labels'][idx]
+    img = transform_train(img)  
+    return img, label
+
+train_loader = DataLoader(BeansDataset('train',transform_train),batch_size = 16,shuffle= True)
+test_loader = DataLoader(BeansDataset('test',transform_train),batch_size = 16,shuffle= True)
+print("Data Ready")
+
+class CropNet(nn.Module):
+    def __init__(self):
+      super().__init__()
+      self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
+      self.bn1 = nn.BatchNorm2d(32)
+      self.pool = nn.MaxPool2d(2)
+      self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
+      self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
+      self.bn2 = nn.BatchNorm2d(64)      
+      self.bn3 = nn.BatchNorm2d(128)     
+      self.fc = nn.Linear(128 * 28 * 28, len(classes))
+  
+        
+    def forward(self, x):  # x=[16,3,224,224]
+        x = self.pool(F.relu(self.bn1(self.conv1(x))))   
+        x = self.pool(F.relu(self.bn2(self.conv2(x))))   
+        x = self.pool(F.relu(self.bn3(self.conv3(x))))  
+        x = x.view(x.size(0), -1)                        
+        return self.fc(x) 
+              
+
+model = CropNet()
+print(model)
+
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')  # Auto GPU!
+print(f"🚀 Using: {device}")
+if device.type == 'cuda':
+    print(f"   GPU: {torch.cuda.get_device_name(0)}")
+    print(f"   Memory: {torch.cuda.get_device_properties(0).total_memory/1e9:.1f}GB")
+
+model.to(device)
+dummy = torch.randn(1,3,224,224).to(device)
+print("Forward OK:", model(dummy).shape)  # torch.Size([1, 3])
+print(f"Classes: {len(classes)}, Model on {device}")
+optimizer = optim.Adam(model.parameters(), lr=0.0003, weight_decay=1e-4)
+criterion = nn.CrossEntropyLoss()
+
+imgs, labs = next(iter(train_loader))
+imgs = imgs.to(device)
+labs = labs.to(device)
+print(f"Batch OK: {imgs.shape}")
+out = model(imgs)
+print(f"Sample logit: {out[0].argmax().item()}")  
+
+for epoch in range(10):
+    model.train()
+    if epoch == 0:
+      print("Sample probs:", F.softmax(out[0], dim=0))  # Vary(not uniform) 
+
+    tot_loss, tot_acc = 0, 0
+    for batch_idx, (imgs, labs) in enumerate(train_loader):
+        imgs, labs = imgs.to(device), labs.to(device)  
+        optimizer.zero_grad()  # Reset grads
+        out = model(imgs)      # Forward pass
+        loss = criterion(out, labs)
+        loss.backward()       
+        optimizer.step()      
+        tot_loss += loss.item()
+        tot_acc += (out.argmax(1)==labs).float().mean()
+    print(f"Ep {epoch+1}: Loss {tot_loss/len(train_loader):.3f} Acc {tot_acc/len(train_loader):.2%}")
+# ===== TEST ACC =====
+model.eval()  
+test_correct = 0
+total_test = 0
+with torch.no_grad():  
+    for imgs, labs in test_loader:  
+        imgs = imgs.to(device)
+        labs = labs.to(device)
+        out = model(imgs)
+        pred = out.argmax(dim=1)  # Highest logit class
+        test_correct += (pred == labs).sum().item()
+        total_test += labs.size(0)
+
+print(f"🎉 Test Acc: {test_correct/total_test*100:.1f}%")
+torch.save(model.state_dict(), 'crop_model.pth')
+
+remedies = {0:"Healthy!",1:"Leaf Spot: Fungicide",2:"Blight: Copper spray",3:"Rust: Neem oil"}
+
+def diagnose(img):  
+    model.eval()
+    img_t = transform_test(img).unsqueeze(0).to(device)
+    with torch.no_grad():
+        probs = model(img_t)[0]
+    pred = probs.argmax().item()
+    conf = probs[pred].item()
+    sev = conf * 100  
+    return f"{classes[pred]}\nConf: {conf:.1%} Sev: {sev:.0f}%\n{remedies[pred]}"
+
+
+torch.save(model, 'crop_doctor_full.pth')  # Full model
+
+app = gr.Interface(fn=diagnose, inputs=gr.Image(type='pil'), outputs='text', title="Crop Disease Detector")
+app.launch(server_port=7860)  
+
+

requirements.txt

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+torch
+torchvision
+datasets
+gradio
+pillow