Create app.py

app.py

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+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
+import gdown
+
+# Google Drive model URLs
+KNN_MODEL_URL = 'https://drive.google.com/uc?id=1TJ0KbzFw-2NfuJf67xvp-32uaYLIqpj3'
+EXTRACTOR_URL = 'https://drive.google.com/uc?id=1HR2Qc8Fji6RzbtG_K_sqSoiG0AQnvyZa'
+
+# Download the model files
+st.write("Downloading models...")
+gdown.download(KNN_MODEL_URL, 'knn_pharyngitis_model.pkl', quiet=False)
+gdown.download(EXTRACTOR_URL, 'mobilenetv2_feature_extractor.h5', quiet=False)
+st.write("Models downloaded successfully!")
+
+# Load the saved models
+knn = joblib.load('knn_pharyngitis_model.pkl')
+feature_extractor = load_model('mobilenetv2_feature_extractor.h5')
+
+# Function to preprocess the uploaded image
+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)
+
+# Function to classify the image
+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}**")