Delete app.py

app.py

@@ -1,93 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-import google.generativeai as genai 
-import os
-import markdown2
-
-# Load the TensorFlow model
-model_path = 'model'
-model = tf.saved_model.load(model_path)
-
-# Configure Gemini API
-api_key = os.getenv("GEMINI_API_KEY")
-genai.configure(api_key=api_key)
-
-labels = ['cataract', 'diabetic_retinopathy', 'glaucoma', 'normal']
-
-def get_disease_detail(disease_name):
-    if disease_name == "normal":
-        prompt = (
-            "Create a text that congratulates having healthy eyes and gives bullet point tips to keep eyes healthy."
-        )
-    else:
-        prompt = (
-            f"Diagnosis: {disease_name}\n\n"
-            "What is it?\n(Description about {disease_name})\n\n"
-            "What causes it?\n(Explain what causes {disease_name})\n\n"
-            "Suggestion\n(Suggestion to user)\n\n"
-            "Reminder: Always seek professional help, such as a doctor."
-        )
-    try:
-        response = genai.GenerativeModel("gemini-1.5-flash").generate_content(prompt)
-        return markdown2.markdown(response.text.strip())
-    except Exception as e:
-        return f"Error: {e}"
-
-def predict_image(image):
-    image_resized = image.resize((224, 224))
-    image_array = np.array(image_resized).astype(np.float32) / 255.0
-    image_array = np.expand_dims(image_array, axis=0)
-
-    predictions = model.signatures['serving_default'](tf.convert_to_tensor(image_array, dtype=tf.float32))['output_0']
-    
-    # Highest prediction
-    top_index = np.argmax(predictions.numpy(), axis=1)[0]
-    top_label = labels[top_index]
-    top_probability = predictions.numpy()[0][top_index]
-
-    explanation = get_disease_detail(top_label)
-
-    return {top_label: top_probability}, explanation
-
-# Example images
-example_images = [
-    ["exp_eye_images/0_right_h.png"],
-    ["exp_eye_images/03fd50da928d_dr.png"],
-    ["exp_eye_images/108_right_h.png"],
-    ["exp_eye_images/1062_right_c.png"],
-    ["exp_eye_images/1084_right_c.png"],
-    ["exp_eye_images/image_1002_g.jpg"]
-]
-
-# Custom CSS for HTML height
-css = """
-.scrollable-html {
-    height: 206px;
-    overflow-y: auto;  
-    border: 1px solid #ccc;  
-    padding: 10px;  
-    box-sizing: border-box;
-}
-"""
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=predict_image,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Label(num_top_classes=1, label="Prediction"), 
-        gr.HTML(label="Explanation", elem_classes=["scrollable-html"])
-    ],
-    examples=example_images,
-    title="Eye Diseases Classifier",
-    description=(
-        "Upload an image of an eye fundus, and the model will predict it.\n\n"
-        "**Disclaimer:** This model is intended as a form of learning process in the field of health-related machine learning and was trained with a limited amount and variety of data with a total of about 4000 data, so the prediction results may not always be correct. There is still a lot of room for improvisation on this model in the future."
-    ),
-    allow_flagging="never",
-    css=css
-)
-
-interface.launch(share=True)