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Browse files- Plant_Disease_Prediction_CNN_Image_Classifier.ipynb +0 -0
- main.py +60 -0
- plant_disease_prediction_model.h5 +3 -0
- requirements.txt +3 -0
- trained_model_link.txt +1 -0
Plant_Disease_Prediction_CNN_Image_Classifier.ipynb
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main.py
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import os
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import json
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from PIL import Image
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import numpy as np
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import tensorflow as tf
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import streamlit as st
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working_dir = os.path.dirname(os.path.abspath(__file__))
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model_path = f"{working_dir}/trained_model/plant_disease_prediction_model.h5"
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# Load the pre-trained model
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model = tf.keras.models.load_model(model_path)
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# loading the class names
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class_indices = json.load(open(f"{working_dir}/class_indices.json"))
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# Function to Load and Preprocess the Image using Pillow
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def load_and_preprocess_image(image_path, target_size=(224, 224)):
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# Load the image
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img = Image.open(image_path)
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# Resize the image
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img = img.resize(target_size)
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# Convert the image to a numpy array
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img_array = np.array(img)
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# Add batch dimension
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img_array = np.expand_dims(img_array, axis=0)
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# Scale the image values to [0, 1]
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img_array = img_array.astype('float32') / 255.
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return img_array
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# Function to Predict the Class of an Image
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def predict_image_class(model, image_path, class_indices):
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preprocessed_img = load_and_preprocess_image(image_path)
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predictions = model.predict(preprocessed_img)
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predicted_class_index = np.argmax(predictions, axis=1)[0]
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predicted_class_name = class_indices[str(predicted_class_index)]
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return predicted_class_name
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# Streamlit App
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st.title('Plant Disease Classifier')
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uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
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if uploaded_image is not None:
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image = Image.open(uploaded_image)
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col1, col2 = st.columns(2)
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with col1:
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resized_img = image.resize((150, 150))
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st.image(resized_img)
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with col2:
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if st.button('Classify'):
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# Preprocess the uploaded image and predict the class
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prediction = predict_image_class(model, uploaded_image, class_indices)
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st.success(f'Prediction: {str(prediction)}')
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plant_disease_prediction_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:b0d196c3af57c9db66072e031922fedef1a0980f2ff5d859ec203f72a31f0646
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size 573706416
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requirements.txt
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numpy==1.26.3
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streamlit==1.30.0
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tensorflow==2.15.0.post1
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trained_model_link.txt
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trained plant disease prediction model link: https://drive.google.com/file/d/1rKh-IElSdHTqax7XdfSdZTn-r8T_qWPf/view?usp=drive_link
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