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app.py

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

requirements.txt

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+streamlit
+tensorflow
+numpy
+google.generativeai 
+Ipython
+openpyxl