Upload 2 files

potato_model.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b731b8339c7f93ee95c941b95cca2bb042c0cb608bba1d0b56e06649d14a6197
+size 2288552

streamlit_app.py

@@ -0,0 +1,52 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+from werkzeug.utils import secure_filename
+import os
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+class_names = ['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']
+
+def predict(model, img):
+    img_array = tf.keras.preprocessing.image.img_to_array(img)
+    img_array = tf.expand_dims(img_array, 0)
+    predictions = model.predict(img_array)
+    predictions_arr = [round(100 * i, 2) for i in predictions[0]]
+    predicted_class = class_names[np.argmax(predictions[0])]
+    confidence = round(100 * (np.max(predictions[0])), 2)
+    return predicted_class, predictions_arr
+
+model = tf.keras.models.load_model('potato_model.h5', compile=False)
+
+def main():
+    st.set_page_config(page_title="Potato Disease Classifier")
+    st.sidebar.title("Potato Disease Classifier")
+    st.sidebar.info("Upload an image of a potato leaf to detect early or late blight.")
+    st.title("Potato Disease Detection")
+    uploaded_file = st.file_uploader("Upload a potato leaf image",type=['jpg','png','jpeg'])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image,caption="Uploaded Image",use_column_width=True)
+        image = image.resize((256,256))
+        img_arr = np.array(image)
+        predicted_class,predictions=predict(model,img_arr)
+
+        response = {
+            "predicted_class": predicted_class,
+            "early": f"{predictions[0]:.2f}%",
+            "late": f"{predictions[1]:.2f}%",
+            "healthy": f"{predictions[2]:.2f}%"
+        }
+
+
+        st.success(f"Predicted Class : {response['predicted_class']}",icon="✅")
+        st.write("Probabilities:")
+        col1,col2,col3 = st.columns(3)
+        col1.metric("Early Blight" , f"{response['early']}", f"{response['early']}")
+        col2.metric("Late Blight" , f"{response['late']}", f"{response['late']}")
+        col3.metric("Healthy" , f"{response['healthy']}", f"{response['healthy']}")
+
+if __name__ == "__main__":
+    main()