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app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+import tensorflow_hub as hub
+from tensorflow.keras import layers
+from tensorflow.keras.models import load_model
+
+# Print versions for debugging
+st.write("TensorFlow version:", tf.__version__)
+
+# model 
+out_len = 10  # Replace this with the actual number of output classes
+
+# Ensure no conflicts with 'model' or 'load_model'
+model_path = 'tomato_model'
+
+# Load your pre-trained model
+try:
+    VIT = load_model(model_path)
+    st.write("Model loaded successfully")
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+
+# Define the class names
+class_names = [
+    '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_Yellow_Leaf_Curl_Virus', 
+    'Tomato_Tomato_mosaic_virus', 'Tomato_healthy'
+]
+
+# Function to load and preprocess the image
+def load_and_prep_image(image):
+    try:
+        img = image.resize((224, 224))  # Assuming your model expects 224x224 images
+        img = np.array(img) / 255.0  # Normalize the image
+        img = np.expand_dims(img, axis=0)  # Add batch dimension
+        return img
+    except Exception as e:
+        st.error(f"Error preprocessing image: {e}")
+        return None
+
+# Streamlit app
+st.title("Tomato Disease Detection")
+st.write("Upload an image of a tomato leaf to detect the disease.")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    try:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        
+        # Preprocess the image
+        prepped_image = load_and_prep_image(image)
+        
+        # Ensure the model is loaded and image is preprocessed before making a prediction
+        if VIT is not None and prepped_image is not None:
+            # Make prediction
+            prediction = VIT.predict(prepped_image)
+            predicted_class = class_names[np.argmax(prediction)]
+            
+            # Display the prediction
+            st.write(f"Prediction: {predicted_class}")
+    except Exception as e:
+        st.error(f"Error during prediction: {e}")

gitattributes

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+*.pb filter=lfs diff=lfs merge=lfs -text
+variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+variables.index filter=lfs diff=lfs merge=lfs -text

gitignore

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+/venv

requirements.txt

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+numpy==1.26.4
+pandas==2.2.2
+pillow==10.3.0
+streamlit==1.35.0
+tensorflow==2.14.0
+tensorflow-hub==0.16.1