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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
import google.generativeai as genai
import os
# 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):
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 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"]
]
# Gradio Interface
interface = gr.Interface(
fn=predict_image,
inputs=gr.Image(type="pil"),
outputs=[
gr.Label(num_top_classes=1, label="Prediction"),
gr.Textbox(label="Explanation", lines=15)
],
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"
)
interface.launch(share=True)
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