new comit

app.py

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+import streamlit as st
+import os
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import torchvision.models as models
+import matplotlib.pyplot as plt
+import seaborn as sns
+from PIL import Image
+from torchvision import datasets
+from torch.utils.data import DataLoader
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
+import random
+
+# ✅ Sidebar Navigation
+st.sidebar.title("Navigation")
+page = st.sidebar.radio("Go to", ["Dataset", "Visualizations", "Model Metrics", "Disease Predictor"])
+
+# ✅ Dataset Path
+DATASET_PATH = "dataset/train"  # Update if needed
+CLASS_NAMES = os.listdir(DATASET_PATH)  # Get class names from folder structure
+
+# ✅ Load Model
+@st.cache_resource
+def load_model():
+    model = models.mobilenet_v2(pretrained=False)
+    model.classifier[1] = nn.Linear(model.classifier[1].in_features, len(CLASS_NAMES))
+    model.load_state_dict(torch.load("plant_disease_model.pth", map_location=torch.device("cpu")))
+    model.eval()
+    return model
+
+model = load_model()
+
+# ✅ Dataset Page – Show Sample Images
+if page == "Dataset":
+    st.title("📊 Dataset Preview")
+    st.write(f"### Classes: {CLASS_NAMES}")
+
+    # Show sample images from each class
+    cols = st.columns(4)
+    for i, class_name in enumerate(CLASS_NAMES[:4]):  # Show 4 classes
+        class_path = os.path.join(DATASET_PATH, class_name)
+        image_name = random.choice(os.listdir(class_path))
+        image_path = os.path.join(class_path, image_name)
+        image = Image.open(image_path)
+        cols[i].image(image, caption=class_name, use_column_width=True)
+
+# ✅ Visualizations Page – Show Class Distribution
+elif page == "Visualizations":
+    st.title("📈 Dataset Visualizations")
+
+    # Count images per class
+    class_counts = {cls: len(os.listdir(os.path.join(DATASET_PATH, cls))) for cls in CLASS_NAMES}
+    
+    # Pie Chart
+    st.write("### Disease Distribution")
+    fig, ax = plt.subplots()
+    ax.pie(class_counts.values(), labels=class_counts.keys(), autopct='%1.1f%%', colors=plt.cm.viridis.colors)
+    st.pyplot(fig)
+
+    # Bar Chart
+    st.write("### Class Count")
+    fig, ax = plt.subplots()
+    sns.barplot(x=list(class_counts.keys()), y=list(class_counts.values()), palette="viridis")
+    plt.xticks(rotation=45)
+    st.pyplot(fig)
+
+# ✅ Model Metrics Page
+elif page == "Model Metrics":
+    st.title("📊 Model Performance")
+
+    # Load True Labels and Predictions
+    y_true = torch.load("y_true.pth")
+    y_pred = torch.load("y_pred.pth")
+
+    # Accuracy
+    accuracy = accuracy_score(y_true, y_pred)
+    st.write(f"### ✅ Accuracy: {accuracy:.2f}")
+
+    # Classification Report
+    st.write("### 📋 Classification Report")
+    report = classification_report(y_true, y_pred, target_names=CLASS_NAMES, output_dict=True)
+    st.write(pd.DataFrame(report).T)
+
+    # Confusion Matrix
+    st.write("### 🔀 Confusion Matrix")
+    cm = confusion_matrix(y_true, y_pred)
+    fig, ax = plt.subplots()
+    sns.heatmap(cm, annot=True, fmt="d", cmap="Blues", xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES)
+    st.pyplot(fig)
+
+# ✅ Disease Predictor Page
+elif page == "Disease Predictor":
+    st.title("🌿 Plant Disease Classifier")
+    
+    # File Upload
+    uploaded_file = st.file_uploader("Upload a plant leaf image", type=["jpg", "png", "jpeg"])
+    
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        # Transform Image
+        transform = transforms.Compose([
+            transforms.Resize((128, 128)),
+            transforms.ToTensor(),
+            transforms.Normalize([0.5], [0.5])
+        ])
+        
+        image_tensor = transform(image).unsqueeze(0)
+
+        # Predict Disease
+        with torch.no_grad():
+            output = model(image_tensor)
+            predicted_class = torch.argmax(output, dim=1).item()
+
+        st.write(f"### ✅ Prediction: {CLASS_NAMES[predicted_class]}")

class_names.txt

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+Pepper__bell___Bacterial_spot
+Pepper__bell___healthy
+PlantVillage
+Potato___Early_blight
+Potato___Late_blight
+Potato___healthy
+Tomato_Bacterial_spot
+Tomato_Early_blight
+Tomato_Late_blight
+Tomato_Leaf_Mold
+Tomato_Septoria_leaf_spot
+Tomato_Spider_mites_Two_spotted_spider_mite
+Tomato__Target_Spot
+Tomato__Tomato_YellowLeaf__Curl_Virus
+Tomato__Tomato_mosaic_virus
+Tomato_healthy

model.py

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+import torch
+import torchvision.transforms as transforms
+from torchvision import models
+from PIL import Image
+
+# ✅ Load Class Names
+with open("class_names.txt", "r") as f:
+    class_names = [line.strip() for line in f.readlines()]
+
+# ✅ Load Trained Model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = models.mobilenet_v2(pretrained=False)
+model.classifier[1] = torch.nn.Linear(model.classifier[1].in_features, len(class_names))
+model.load_state_dict(torch.load("plant_disease_model.pth", map_location=device))
+model = model.to(device)
+model.eval()
+
+# ✅ Image Transformations (Must match training settings)
+transform = transforms.Compose([
+    transforms.Resize((128, 128)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5], [0.5])
+])
+
+# ✅ Function to Make Predictions
+def predict_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    image = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        output = model(image)
+        predicted_class = torch.argmax(output, dim=1).item()
+
+    return class_names[predicted_class]
+
+# ✅ Test the model (optional)
+if __name__ == "__main__":
+    sample_image = "test_image.jpg"  # Replace with an actual image path
+    prediction = predict_image(sample_image)
+    print(f"Predicted Class: {prediction}")

plant_disease_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:892a520120fdb7827ff6a507639de765df7a1de817b9b56b140349977c802ed5
+size 9221158

requirements.txt

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+torch
+torchvision
+Pillow
+tqdm
+streamlit