Upload 8 files

class_indices.json

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+{"0": "Apple___Apple_scab", "1": "Apple___Black_rot", "2": "Apple___Cedar_apple_rust", "3": "Apple___healthy", "4": "Blueberry___healthy", "5": "Cherry_(including_sour)___Powdery_mildew", "6": "Cherry_(including_sour)___healthy", "7": "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "8": "Corn_(maize)___Common_rust_", "9": "Corn_(maize)___Northern_Leaf_Blight", "10": "Corn_(maize)___healthy", "11": "Grape___Black_rot", "12": "Grape___Esca_(Black_Measles)", "13": "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "14": "Grape___healthy", "15": "Orange___Haunglongbing_(Citrus_greening)", "16": "Peach___Bacterial_spot", "17": "Peach___healthy", "18": "Pepper,_bell___Bacterial_spot", "19": "Pepper,_bell___healthy", "20": "Potato___Early_blight", "21": "Potato___Late_blight", "22": "Potato___healthy", "23": "Raspberry___healthy", "24": "Soybean___healthy", "25": "Squash___Powdery_mildew", "26": "Strawberry___Leaf_scorch", "27": "Strawberry___healthy", "28": "Tomato___Bacterial_spot", "29": "Tomato___Early_blight", "30": "Tomato___Late_blight", "31": "Tomato___Leaf_Mold", "32": "Tomato___Septoria_leaf_spot", "33": "Tomato___Spider_mites Two-spotted_spider_mite", "34": "Tomato___Target_Spot", "35": "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "36": "Tomato___Tomato_mosaic_virus", "37": "Tomato___healthy"}

config.toml

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+[global]
+# If True, will show a warning when you run a Streamlit-enabled script via "python my_script.py".
+# Default: true
+showWarningOnDirectExecution = true
+
+[logger]
+# Level of logging: 'error', 'warning', 'info', or 'debug'.
+# Default: 'info'
+level = "debug"
+
+
+
+[runner]
+# Allows you to type a variable or string by itself in a single line of Python code to write it to the app.
+# Default: true
+magicEnabled = true
+
+
+
+[server]
+# List of folders that should not be watched for changes. Relative paths will be taken as relative to the current working directory.
+# Example: ['/home/user1/env', 'relative/path/to/folder']
+# Default: []
+folderWatchBlacklist = ['']
+
+# If false, will attempt to open a browser window on start.
+# Default: false unless (1) we are on a Linux box where DISPLAY is unset, or (2) server.liveSave is set.
+headless = true
+
+# Immediately share the app in such a way that enables live monitoring, and post-run analysis.
+# Default: false
+liveSave = false
+
+# Automatically rerun script when the file is modified on disk.
+# Default: false
+runOnSave = false
+
+# The port where the server will listen for client and browser connections.
+# Default: 8501
+port = 80
+
+# Enables support for Cross-Origin Request Sharing, for added security.
+# Default: true
+enableCORS = false
+
+[browser]
+# Internet address of the server server that the browser should connect to. Can be IP address or DNS name.
+# Default: 'localhost'
+serverAddress = "0.0.0.0"
+
+# Whether to send usage statistics to Streamlit.
+# Default: true
+gatherUsageStats = true
+
+# Port that the browser should use to connect to the server when in liveSave mode.
+# Default: whatever value is set in server.port.
+serverPort = 80
+

credentials.toml

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+[general]
+email=""
+

main.py

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+import os
+import json
+from PIL import Image
+
+import numpy as np
+import tensorflow as tf
+import streamlit as st
+
+
+working_dir = os.path.dirname(os.path.abspath(__file__))
+model_path = f"{working_dir}/trained_model/plant_disease_prediction_model.h5"
+# Load the pre-trained model
+model = tf.keras.models.load_model(model_path)
+
+# loading the class names
+class_indices = json.load(open(f"{working_dir}/class_indices.json"))
+
+
+# Function to Load and Preprocess the Image using Pillow
+def load_and_preprocess_image(image_path, target_size=(224, 224)):
+    # Load the image
+    img = Image.open(image_path)
+    # Resize the image
+    img = img.resize(target_size)
+    # Convert the image to a numpy array
+    img_array = np.array(img)
+    # Add batch dimension
+    img_array = np.expand_dims(img_array, axis=0)
+    # Scale the image values to [0, 1]
+    img_array = img_array.astype('float32') / 255.
+    return img_array
+
+
+# Function to Predict the Class of an Image
+def predict_image_class(model, image_path, class_indices):
+    preprocessed_img = load_and_preprocess_image(image_path)
+    predictions = model.predict(preprocessed_img)
+    predicted_class_index = np.argmax(predictions, axis=1)[0]
+    predicted_class_name = class_indices[str(predicted_class_index)]
+    return predicted_class_name
+
+
+# Streamlit App
+st.title('Plant Disease Classifier')
+
+uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    col1, col2 = st.columns(2)
+
+    with col1:
+        resized_img = image.resize((150, 150))
+        st.image(resized_img)
+
+    with col2:
+        if st.button('Classify'):
+            # Preprocess the uploaded image and predict the class
+            prediction = predict_image_class(model, uploaded_image, class_indices)
+            st.success(f'Prediction: {str(prediction)}')

plant_disease_prediction_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b0d196c3af57c9db66072e031922fedef1a0980f2ff5d859ec203f72a31f0646
+size 573706416

requirements.txt

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+numpy==1.26.3
+streamlit==1.30.0
+tensorflow==2.15.0.post1

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