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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+trained_plant_disease_model.keras filter=lfs diff=lfs merge=lfs -text

main.py

@@ -0,0 +1,55 @@
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+def model_prediction(test_image):
+    model = tf.keras.models.load_model("trained_plant_disease_model.keras")
+    image = tf.keras.preprocessing.image.load_img(test_image,target_size=(128,128))
+    input_arr = tf.keras.preprocessing.image.img_to_array(image)
+    input_arr = np.array([input_arr]) #convert single image to batch
+    predictions = model.predict(input_arr)
+    return np.argmax(predictions) #return index of max element
+
+#Sidebar
+st.sidebar.title("AgriSens")
+app_mode = st.sidebar.selectbox("Select Page",["HOME","DISEASE RECOGNITION"])
+#app_mode = st.sidebar.selectbox("Select Page",["Home","About","Disease Recognition"])
+
+# import Image from pillow to open images
+from PIL import Image
+img = Image.open("Diseases.png")
+
+# display image using streamlit
+# width is used to set the width of an image
+st.image(img)
+
+#Main Page
+if(app_mode=="HOME"):
+    st.markdown("<h1 style='text-align: center;'>SMART DISEASE DETECTION", unsafe_allow_html=True)
+    
+#Prediction Page
+elif(app_mode=="DISEASE RECOGNITION"):
+    st.header("DISEASE RECOGNITION")
+    test_image = st.file_uploader("Choose an Image:")
+    if(st.button("Show Image")):
+        st.image(test_image,width=4,use_column_width=True)
+    #Predict button
+    if(st.button("Predict")):
+        st.snow()
+        st.write("Our Prediction")
+        result_index = model_prediction(test_image)
+        #Reading Labels
+        class_name = ['Apple___Apple_scab', 'Apple___Black_rot', 'Apple___Cedar_apple_rust', 'Apple___healthy',
+                    'Blueberry___healthy', 'Cherry_(including_sour)___Powdery_mildew', 
+                    'Cherry_(including_sour)___healthy', 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot', 
+                    'Corn_(maize)___Common_rust_', 'Corn_(maize)___Northern_Leaf_Blight', 'Corn_(maize)___healthy', 
+                    'Grape___Black_rot', 'Grape___Esca_(Black_Measles)', 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)', 
+                    'Grape___healthy', 'Orange___Haunglongbing_(Citrus_greening)', 'Peach___Bacterial_spot',
+                    'Peach___healthy', 'Pepper,_bell___Bacterial_spot', 'Pepper,_bell___healthy', 
+                    'Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy', 
+                    'Raspberry___healthy', 'Soybean___healthy', 'Squash___Powdery_mildew', 
+                    'Strawberry___Leaf_scorch', 'Strawberry___healthy', 'Tomato___Bacterial_spot', 
+                    'Tomato___Early_blight', 'Tomato___Late_blight', 'Tomato___Leaf_Mold', 
+                    'Tomato___Septoria_leaf_spot', 'Tomato___Spider_mites Two-spotted_spider_mite', 
+                    'Tomato___Target_Spot', 'Tomato___Tomato_Yellow_Leaf_Curl_Virus', 'Tomato___Tomato_mosaic_virus',
+                      'Tomato___healthy']
+        st.success("Model is Predicting it's a {}".format(class_name[result_index]))

trained_plant_disease_model.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2958851ea32bd3e01e131f1042a2eb7e64bd719b7fc4470dbdbd1e513386201
+size 94195019