Delete app.py

app.py

@@ -1,69 +0,0 @@
-import streamlit as st
-import numpy as np
-from PIL import Image
-import tensorflow as tf
-import tensorflow_hub as hub
-from tensorflow.keras import layers
-from tensorflow.keras.models import load_model
-
-# Print versions for debugging
-st.write("TensorFlow version:", tf.__version__)
-
-# model 
-out_len = 10  # Replace this with the actual number of output classes
-
-# Ensure no conflicts with 'model' or 'load_model'
-model_path = 'tomato_model'
-
-# Load your pre-trained model
-try:
-    VIT = load_model(model_path)
-    st.write("Model loaded successfully")
-except Exception as e:
-    st.error(f"Error loading model: {e}")
-
-# Define the class names
-class_names = [
-    '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'
-]
-
-# Function to load and preprocess the image
-def load_and_prep_image(image):
-    try:
-        img = image.resize((224, 224))  # Assuming your model expects 224x224 images
-        img = np.array(img) / 255.0  # Normalize the image
-        img = np.expand_dims(img, axis=0)  # Add batch dimension
-        return img
-    except Exception as e:
-        st.error(f"Error preprocessing image: {e}")
-        return None
-
-# Streamlit app
-st.title("Tomato Disease Detection")
-st.write("Upload an image of a tomato leaf to detect the disease.")
-
-# File uploader
-uploaded_file = st.file_uploader("Choose an image...", type="jpg")
-
-if uploaded_file is not None:
-    try:
-        # Display the uploaded image
-        image = Image.open(uploaded_file)
-        st.image(image, caption='Uploaded Image', use_column_width=True)
-        
-        # Preprocess the image
-        prepped_image = load_and_prep_image(image)
-        
-        # Ensure the model is loaded and image is preprocessed before making a prediction
-        if VIT is not None and prepped_image is not None:
-            # Make prediction
-            prediction = VIT.predict(prepped_image)
-            predicted_class = class_names[np.argmax(prediction)]
-            
-            # Display the prediction
-            st.write(f"Prediction: {predicted_class}")
-    except Exception as e:
-        st.error(f"Error during prediction: {e}")