Create app.py

app.py

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+# Load model directly
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("rararara9999/Model")
+model = AutoModelForSequenceClassification.from_pretrained("rararara9999/Model")
+
+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+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...")
+
+        # 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")
+
+        # 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]
+        confidence = predictions[max_index]
+
+        st.write(f"Predicted Label: {predicted_label}")
+        st.write(f"Confidence: {confidence:.2f}")
+
+if __name__ == "__main__":
+    main()