Add application to deploy

.gitignore

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+*.idea

app.py

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+import streamlit as st
+from fastai.vision.all import *
+import pathlib
+plt = platform.system()
+if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
+
+current_dir = os.getcwd()
+model = 'resnet_model_14_Sep_1.pkl'
+model_path = os.path.join(current_dir, model)
+
+model = load_learner(model_path)
+
+
+st.title("Defect Classification")
+st.write("Upload an image to check whether if it's normal or defective (stain)")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Perform Predictions
+    if st.button("Predict"):
+        img = PILImage.create(uploaded_file)
+        # Make predictions
+        predictions, _ = model.get_preds(dl=model.dls.test_dl([img]))
+        # Get the predicted class index
+        predicted_class_idx = predictions.argmax(dim=1).item()
+        # Get the confidence score for the predicted class
+        confidence_score = predictions[0][predicted_class_idx].item()
+        # Map the class index to class name
+        class_names = model.dls.vocab
+        predicted_class = class_names[predicted_class_idx]
+
+        # Display the prediction and confidence score
+        st.write(f"Prediction: {predicted_class}")
+        st.write(f"Confidence: {confidence_score:.4f}")
+

requirements.txt

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+streamlit==1.26.0
+fastai==2.7.12

resnet_model_17_Sep_1.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ba5ac0313e80c5736cede33bc749ce6c0a1f237f9a4d7482360f0cdc19c1db00
+size 47002013