Update app.py

app.py

@@ -4,52 +4,37 @@ 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
 
+# Paths to the saved models
+KNN_MODEL_PATH = './knn_pharyngitis_model.pkl'
+EXTRACTOR_PATH = './mobilenetv2_feature_extractor.h5'
 
-# 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)
-
+# Load the saved models
+st.write("Loading models...")
+knn = joblib.load(KNN_MODEL_PATH)
+feature_extractor = load_model(EXTRACTOR_PATH)
+st.write("Models loaded successfully!")
 
+# 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)
@@ -58,4 +43,4 @@ if uploaded_file is not None:
     # Classify the image
     st.write("Classifying...")
     prediction = classify_image(image)
-    st.write(f"Prediction: **{prediction}**")
+    st.write(f"Prediction: **{prediction}**")