Update app.py

app.py

@@ -1,13 +1,45 @@
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import subprocess
 
-tokenizer = AutoTokenizer.from_pretrained("rararara9999/Model")
-model = AutoModelForSequenceClassification.from_pretrained("rararara9999/Model")
+# 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"])
 
-import streamlit as st
-from transformers import pipeline
+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:")
@@ -19,10 +51,6 @@ def main():
         st.write("")
         st.write("Classifying...")
 
-        # Load the fine-tuned model and image processor
-        model_checkpoint = "rararara9999/Model"
-        image_processor = AutoImageProcessor.from_pretrained(model_checkpoint)
-
         # Preprocess the image
         inputs = image_processor(images=image, return_tensors="pt")
 
@@ -42,3 +70,4 @@ def main():
 
 if __name__ == "__main__":
     main()
+