Update app.py

app.py

@@ -1,29 +1,71 @@
+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)
 
-# 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")
+# Standalone Test Script
+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()
+        # 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]
+        # 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]
 
-print(f"The predicted label is {predicted_label}")
+        st.write(f"Predicted Label: {predicted_label}")
+        st.write(f"Confidence: {confidence:.2f}")
 
+if __name__ == "__main__":
+    main()