app.py

import streamlit as st
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
import joblib
from huggingface_hub import hf_hub_url, cached_download

# Replace with your Space name (from the link)
SPACE_NAME = "engrharis/Throat_Image_Classifier"

# Assuming the filenames are the same as before 
KNN_MODEL_FILE = "knn_pharyngitis_model.pkl"
EXTRACTOR_FILE = "mobilenetv2_feature_extractor.h5"


def download_models(url, filename):
    """Downloads model files from Hugging Face space if not cached locally."""
    model_path = hf_hub_url(SPACE_NAME, filename=filename)
    if not cached_download(model_path):
        st.write(f"Downloading {filename}...")
        cached_download(model_path)
        st.write(f"{filename} downloaded successfully!")


# Load the saved models (download if not cached)
download_models(SPACE_NAME, KNN_MODEL_FILE)
download_models(SPACE_NAME, EXTRACTOR_FILE)

knn = joblib.load(KNN_MODEL_FILE)
feature_extractor = load_model(EXTRACTOR_FILE)


def preprocess_image(image):
    img = image.resize((224, 224))  # Resize to match MobileNetV2 input size
    img_array = np.array(img)
    img_array = preprocess_input(img_array)  # Apply MobileNetV2 preprocessing
    return np.expand_dims(img_array, axis=0)


def classify_image(image):
    processed_image = preprocess_image(image)
    features = feature_extractor.predict(processed_image)
    prediction = knn.predict(features)
    return "Pharyngitis" if prediction[0] == 1 else "No Pharyngitis"


# Streamlit app UI
st.title("Pharyngitis Classification App")
st.write("Upload an image to classify it as 'Pharyngitis' or 'No Pharyngitis'.")
uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
    # Load the uploaded image
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Classify the image
    st.write("Classifying...")
    prediction = classify_image(image)
    st.write(f"Prediction: **{prediction}**")