Delete app

app/Dockerfile

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-# This sets up the container with Python 3.10 installed.
-FROM python:3.10-slim
-
-# This copies everything in your current directory to the /app directory in the container.
-COPY . /app
-
-# This sets the /app directory as the working directory for any RUN, CMD, ENTRYPOINT, or COPY instructions that follow.
-WORKDIR /app
-
-# This runs pip install for all the packages listed in your requirements.txt file.
-RUN pip install -r requirements.txt
-
-# This tells Docker to listen on port 80 at runtime. Port 80 is the standard port for HTTP.
-EXPOSE 80
-
-# This command creates a .streamlit directory in the home directory of the container.
-RUN mkdir ~/.streamlit
-
-# This copies your Streamlit configuration file into the .streamlit directory you just created.
-RUN cp config.toml ~/.streamlit/config.toml
-
-# Similar to the previous step, this copies your Streamlit credentials file into the .streamlit directory.
-RUN cp credentials.toml ~/.streamlit/credentials.toml
-
-# This sets the default command for the container to run the app with Streamlit.
-ENTRYPOINT ["streamlit", "run"]
-
-# This command tells Streamlit to run your app.py script when the container starts.
-CMD ["main.py"]

app/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"}

app/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
-

app/credentials.toml

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-[general]
-email=""
-

app/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)}')

app/requirements.txt

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

app/trained_model/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