Update app.py

app.py

@@ -1,73 +1,29 @@
-import subprocess
-
-# Install the required packages
-subprocess.check_call(["pip", "install", "--upgrade", "pip"])
-subprocess.check_call(["pip", "install", "-U", "transformers"])
-subprocess.check_call(["pip", "install", "-U", "accelerate"])
-subprocess.check_call(["pip", "install", "datasets"])
-subprocess.check_call(["pip", "install", "evaluate"])
-subprocess.check_call(["pip", "install", "scikit-learn"])
-subprocess.check_call(["pip", "install", "torchvision"])
-
 from transformers import AutoModelForImageClassification, AutoImageProcessor
 import torch
 import numpy as np
 from PIL import Image
-import streamlit as st
 
 # Load the fine-tuned model and image processor
 model_checkpoint = "rararara9999/Model"
 model = AutoModelForImageClassification.from_pretrained(model_checkpoint, num_labels=2)
 image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)
 
-# Standalone Test Script
-image_path = "C:\Users\crc96\Desktop\HKUST\testing_picture"
-def test_model(image_path):
-    # Load and preprocess the image
-    image = Image.open(image_path)
-    inputs = image_processor(images=image, return_tensors="pt")
-
-    # Get model predictions
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-    predictions = predictions.cpu().detach().numpy()
-
-    # Get the index of the largest output value
-    max_index = np.argmax(predictions)
-    labels = ["Wearing Mask", "Not Wearing Mask"]
-    predicted_label = labels[max_index]
-
-    print(f"The predicted label is {predicted_label}")
-
-# Streamlit App for Interactive Testing
-def main():
-    st.title("Face Mask Detection with HuggingFace Spaces")
-    st.write("Upload an image to analyze whether the person is wearing a mask:")
-
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file)
-        st.image(image, caption='Uploaded Image.', use_column_width=True)
-        st.write("")
-        st.write("Classifying...")
-
-        # Preprocess the image
-        inputs = image_processor(images=image, return_tensors="pt")
-
-        # Get model predictions
-        outputs = model(**inputs)
-        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        predictions = predictions.cpu().detach().numpy()
+# Load and preprocess the image
+image_path = r"C:\Users\crc96\Desktop\HKUST\testing_picture"  # Using raw string
+# or
+# image_path = "C:/Users/crc96/Desktop/HKUST/testing_picture"  # Using forward slashes
+image = Image.open(image_path)
+inputs = image_processor(images=image, return_tensors="pt")
 
-        # Get the index of the largest output value
-        max_index = np.argmax(predictions)
-        labels = ["Wearing Mask", "Not Wearing Mask"]
-        predicted_label = labels[max_index]
-        confidence = predictions[max_index]
+# Get model predictions
+outputs = model(**inputs)
+predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+predictions = predictions.cpu().detach().numpy()
 
-        st.write(f"Predicted Label: {predicted_label}")
-        st.write(f"Confidence: {confidence:.2f}")
+# Get the index of the largest output value
+max_index = np.argmax(predictions)
+labels = ["Wearing Mask", "Not Wearing Mask"]
+predicted_label = labels[max_index]
 
-if __name__ == "__main__":
-    main()
+print(f"The predicted label is {predicted_label}")