Upload main.py

main.py

@@ -0,0 +1,58 @@
+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}/model/plant_disease_prediction_model.h5"
+
+# Loading 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 Classification')
+
+uploaded_image = st.file_uploader("Upload an Plant 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)}')