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import streamlit as st
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import tensorflow as tf
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from PIL import Image
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import numpy as np
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import os
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import pandas as pd
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import io
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def import_and_predict(image_data, model, class_labels):
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size = (256, 256)
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if image_data is not None:
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image = Image.open(io.BytesIO(image_data.read()))
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image = image.resize(size)
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image = np.array(image)
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img_reshape = image / 255.0
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img_reshape = np.expand_dims(img_reshape, axis=0)
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prediction = model.predict(img_reshape)
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st.image(image, width=300)
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predictions_label = class_labels[np.argmax(prediction[0])]
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return predictions_label
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else:
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st.warning("Please upload an image.")
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return None
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st.title("Plant Disease Detection")
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selected_item = st.radio("Select an item:", ["Apple", "Mango", "Grape", "Tomato"])
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if selected_item:
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st.write(f"You selected: {selected_item}")
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uploaded_image = st.file_uploader(f"Upload an image of {selected_item.lower()}", type=["jpg", "jpeg", "png"])
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df = pd.read_excel('./final format.xlsx')
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models_path = [
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'./models/best_model_50_apple_plant (1).h5','./models/best_model_100_subset (1).h5','./models/best_model_50_grape_plant2.h5','./models/model_inception_epoch_50_mango.h5']
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@st.cache_resource
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def load_model(models_path):
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model = tf.keras.models.load_model(models_path)
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return model
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if selected_item == 'Apple':
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CLASS_LABELS = ['apple scab', 'Apple black rot', 'cedar apple rust', 'Apple healthy']
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model =load_model(models_path[0])
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prediction = import_and_predict(uploaded_image, model, CLASS_LABELS)
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st.write("disease name: ", prediction)
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if prediction != None:
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new_title = '<p style="font-size: 38px">Measures you can take to control: </p>'
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st.markdown(new_title, unsafe_allow_html=True)
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if prediction == CLASS_LABELS[0]:
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response = df['Measures'].dropna().head(28)
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[1]:
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response = df['Measures'].dropna()[28:46]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[2]:
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response = df['Measures'].dropna()[46:63]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[3]:
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st.write("Plant is healthy take good care of it")
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elif selected_item == 'Tomato':
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CLASS_LABELS = ['Tomato Early blight', 'Tomato Leaf Mold', 'Tomato YellowLeaf Curl Virus',
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'Tomato mosaic virus', 'Tomato healthy']
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model =load_model(models_path[1])
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prediction = import_and_predict(uploaded_image, model, CLASS_LABELS)
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st.write("disease name: ", prediction)
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if prediction != None:
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new_title = '<p style="font-size: 38px">Measures you can take to control: </p>'
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st.markdown(new_title, unsafe_allow_html=True)
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if prediction == CLASS_LABELS[0]:
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response = df['Measures'].dropna()[63:79]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[1]:
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response = df['Measures'].dropna()[187:196]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[2]:
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response = df['Measures'].dropna()[99:113]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[3]:
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response = df['Measures'].dropna()[79:99]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[4]:
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st.write("Plant is healthy take good care of it")
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elif selected_item == 'Grape':
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CLASS_LABELS = ['Grape Black rot', 'Grape Black Measles','Grape Leaf blight', 'Grape healthy']
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model =load_model(models_path[2])
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prediction = import_and_predict(uploaded_image, model, CLASS_LABELS)
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st.write("disease name: ", prediction)
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if prediction != None:
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new_title = '<p style="font-size: 38px">Measures you can take to control: </p>'
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st.markdown(new_title, unsafe_allow_html=True)
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if prediction == CLASS_LABELS[0]:
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response = df['Measures'].dropna()[123:134]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[1]:
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response = df['Measures'].dropna()[196:204]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[2]:
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response = df['Measures'].dropna()[113:123]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[3]:
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st.write("Plant is healthy take good care of it")
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elif selected_item == 'Mango':
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CLASS_LABELS = ['Anthracnose', 'Bacterial Canker', 'Cutting Weevil', 'Die Back', 'Gall Midge', 'Healthy','Sooty Mould' ,'Powdery Mildew']
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model =load_model(models_path[3])
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prediction = import_and_predict(uploaded_image, model, CLASS_LABELS)
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st.write("disease name: ", prediction)
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if prediction != None:
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new_title = '<p style="font-size: 38px">Measures you can take to control: </p>'
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st.markdown(new_title, unsafe_allow_html=True)
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if prediction == CLASS_LABELS[0]:
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response = df['Measures'].dropna()[134:151]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[1]:
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response = df['Measures'].dropna()[151:165]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[2]:
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response = df['Measures'].dropna()[164:173]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[3]:
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response = df['Measures'].dropna()[172:180]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[4]:
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response = df['Measures'].dropna()[181:189]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[5]:
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st.write("Plant is healthy take good care of it")
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elif prediction == CLASS_LABELS[6]:
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response = df['Measures'].dropna()[164:174]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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elif prediction == CLASS_LABELS[7]:
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response = df['Measures'].dropna()[174:189]
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response_text = " ".join([f"<p style='font-size: 18px;'>{text}</p>" for text in response])
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st.write(response_text, unsafe_allow_html=True)
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