added model

app.py

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+# detect.py
+import torch
+import torchvision.transforms as transforms
+from torchvision.models import resnet50
+from PIL import Image
+import torch.nn as nn
+
+# Define the class names - make sure these match your training classes
+CLASS_NAMES = [
+    "Apple___Apple_scab",
+    "Apple___Black_rot",
+    # Add all your class names here...
+]
+
+def load_model(model_path):
+    # Initialize the model architecture
+    model = resnet50(pretrained=False)
+    num_classes = len(CLASS_NAMES)
+    model.fc = nn.Linear(model.fc.in_features, num_classes)
+    
+    # Load the state dict
+    state_dict = torch.load(model_path, map_location=torch.device('cpu'))
+    model.load_state_dict(state_dict)
+    model.eval()
+    return model
+
+def predict_image(image_path, model):
+    """Predict the class of a given image"""
+    # Define the same transform as used during training
+    transform = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+    ])
+    
+    # Load and preprocess the image
+    image = Image.open(image_path).convert('RGB')
+    image_tensor = transform(image).unsqueeze(0)
+    
+    # Make prediction
+    with torch.no_grad():
+        outputs = model(image_tensor)
+        _, predicted = torch.max(outputs, 1)
+        
+    return CLASS_NAMES[predicted.item()]
+
+# streamlit_app.py
+import streamlit as st
+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import os
+from detect import load_model, predict_image, CLASS_NAMES
+
+# Set page config
+st.set_page_config(page_title="Plant Disease Predictor", page_icon="🍃", layout="wide")
+
+# Load the model
+@st.cache_resource
+def load_model_cached():
+    model_path = 'models/leaf_disease_res50_model_epoch_10.pth'
+    model = load_model(model_path)
+    return model
+
+# Load model at startup
+model = load_model_cached()
+
+# Streamlit app
+st.title("Plant Disease Predictor")
+st.write("Upload an image of a plant leaf to predict if it has a disease.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    if st.button('Predict'):
+        # Show prediction in progress
+        with st.spinner('Analyzing image...'):
+            # Save the uploaded file temporarily
+            with open("temp_image.jpg", "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            
+            # Make prediction
+            prediction = predict_image("temp_image.jpg", model)
+            
+            # Remove temporary file
+            os.remove("temp_image.jpg")
+            
+            # Display result
+            st.success(f"Prediction: {prediction}")
+            
+            # Display confidence scores
+            transform = transforms.Compose([
+                transforms.Resize((224, 224)),  # Match the training size
+                transforms.ToTensor(),
+            ])
+            
+            with torch.no_grad():
+                img_tensor = transform(image).unsqueeze(0)
+                outputs = model(img_tensor)
+                probabilities = torch.nn.functional.softmax(outputs[0], dim=0)
+            
+            # Display top 5 predictions
+            top5_prob, top5_catid = torch.topk(probabilities, 5)
+            st.write("Top 5 Predictions:")
+            for i in range(top5_prob.size(0)):
+                st.write(f"{CLASS_NAMES[top5_catid[i]]}: {top5_prob[i].item()*100:.2f}%")
+
+# Display list of detectable diseases
+st.write("## List of Detectable Plant Diseases")
+st.write("This model can detect the following plant diseases:")
+for disease in CLASS_NAMES:
+    st.write(f"- {disease.replace('___', ' - ')}")

models/leaf_disease_res50_model_epoch_10.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6550ff3036e8f9503a549a70ef3b6790a283211fd2e87f97a306194e2f5d6eda
+size 94663438