Delete app.py

app.py

@@ -1,64 +0,0 @@
-import os
-import streamlit as st
-import tensorflow as tf
-import numpy as np
-
-# Loading the saved model
-model = tf.keras.models.load_model('model.h5')
-
-def predict(input_image):
-    try:
-        # Preprocessing
-        input_image = tf.convert_to_tensor(input_image)
-        input_image = tf.image.resize(input_image, [224, 224])
-        input_image = tf.expand_dims(input_image, 0) / 255.0
-
-        # Prediction
-        predictions = model.predict(input_image)
-        labels = ['Cataract', 'Conjunctivitis', 'Glaucoma', 'Normal']
-
-        # Get confidence score for each class
-        disease_confidence = {label: np.round(predictions[0][idx] * 100, 3) for idx, label in enumerate(labels)}
-
-        # Get confidence percentage for the "Normal" class
-        normal_confidence = disease_confidence['Normal']
-
-        # Check if Normal confidence is greater than 50%
-        if normal_confidence > 50:
-            return f"""Congrats! no disease detected 
-            Normal with confidence: {normal_confidence}%"""
-
-
-        output_lines = [f"\n{disease}: {confidence}%" for disease, confidence in disease_confidence.items()]
-        output_string = "\n".join(output_lines[:-1])
-        return output_string
-
-
-    except Exception as e:
-        return f"An error occurred: {e}"
-
-# Example images directory
-examples = [os.path.join("example", file) for file in os.listdir("example")]
-
-# Streamlit app
-st.title("👁️ Eye Disease Detection")
-st.write("This model identifies common eye diseases such as Cataract, Conjunctivitis, and Glaucoma. Upload an eye image to see how the model classifies its condition.")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png"])
-
-if uploaded_file is not None:
-    # Display the uploaded image
-    image = tf.image.decode_image(uploaded_file.read(), channels=3)
-    image_np = image.numpy()
-    st.image(image_np, caption='Uploaded Image.', use_column_width=True)
-
-    # Perform prediction
-    prediction = predict(image_np)
-    st.write("Prediction:")
-    st.write(prediction)
-
-# Display examples images
-st.write("Examples:")
-cols = st.columns(len(examples))
-for idx, example in enumerate(examples):
-    cols[idx].image(example, caption=os.path.basename(example))