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| import streamlit as st | |
| import tempfile | |
| from PIL import Image | |
| import os | |
| import tensorflow as tf | |
| import numpy as np | |
| import io | |
| import json | |
| from google import genai | |
| from dotenv import dotenv_values, load_dotenv | |
| load_dotenv() | |
| config = dotenv_values(".env") | |
| MODEL_ID = "gemini-2.0-flash" | |
| model_id = MODEL_ID | |
| client = genai.Client(api_key = config["GEMINI_API_KEY"]) | |
| # Load labels from JSON file | |
| with open('model/labels.json', 'r') as f: | |
| labels = json.load(f) | |
| # Load TensorFlow Lite model | |
| def load_tflite_model(): | |
| interpreter = tf.lite.Interpreter(model_path="model/model.tflite") | |
| interpreter.allocate_tensors() | |
| return interpreter | |
| # Get input and output details | |
| interpreter = load_tflite_model() | |
| input_details = interpreter.get_input_details() | |
| output_details = interpreter.get_output_details() | |
| def main(): | |
| st.title("Skin Cancer Classifier") | |
| img_file = None | |
| image_path = "" | |
| # Upload an image | |
| img_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"]) | |
| if img_file is not None: | |
| # Save the image to a temporary file | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_file: | |
| temp_file.write(img_file.read()) | |
| image_path = temp_file.name | |
| st.write("Image saved to:", image_path) | |
| st.image(image_path, use_container_width=True) | |
| if st.button("Classify"): | |
| with st.spinner("Processing..."): | |
| image = Image.open(io.BytesIO(img_file.getbuffer())).convert('RGB') | |
| # Get model input shape | |
| input_shape = input_details[0]['shape'] | |
| # Preprocess the image | |
| image = image.resize((input_shape[1], input_shape[2])) | |
| image_array = np.array(image, dtype=np.float32) | |
| image_array = image_array / 255.0 | |
| image_array = np.expand_dims(image_array, axis=0) | |
| # Make prediction | |
| interpreter.set_tensor(input_details[0]['index'], image_array) | |
| interpreter.invoke() | |
| # Get prediction results | |
| prediction = interpreter.get_tensor(output_details[0]['index']) | |
| # Get the predicted class index | |
| predicted_class_index = np.argmax(prediction[0]) | |
| # Get the corresponding label information | |
| predicted_label = labels[predicted_class_index] | |
| genai_response = client.models.generate_content( | |
| model=MODEL_ID, | |
| contents=[ | |
| "You are a medical encyclopedia. You are given a skin cancer image and you need to provide a detailed explanation of the disease and its treatment.", | |
| f"The predicted class is {predicted_label['name']}. Provide a detailed explanation of the disease, its treatment, and prevention.", | |
| "Start with the title: Classfification of Skin Cancer", | |
| ] | |
| ) | |
| # Display Gemini response | |
| st.markdown(genai_response.text) | |
| if __name__ == "__main__": | |
| main() | |