Delete main.py

main.py

@@ -1,58 +0,0 @@
-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)}')