Add application file

app.py

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+import gradio as gr
+from huggingface_hub import hf_hub_download
+from fastai.learner import load_learner
+
+from fastai.vision.all import *
+
+
+class_list = ['BacterialPneumonia','COVID-19','Normal','ViralPneumonia']
+
+def label_func(file):
+    print(f"file: {file}")
+    return "Normal" #file.parent.name
+
+model = load_learner(
+    hf_hub_download("kmknair/xray-classification", "xray-classification-export.pkl")
+)
+
+
+def predict_image(file_path):
+    # img = PILImage.create(Path(file_path))
+    results = model.predict(file_path)
+    pred_class = class_list[results[1].item()]
+    pred_probs = f"{100*results[2][results[1].item()]:.2f}"
+    result_text = f"Predicted Results : {pred_class} probabilty: {pred_probs}"
+    print(result_text)
+    return result_text
+
+
+
+xray_file_tab = gr.Interface(
+    fn=predict_image,
+    inputs=[
+        gr.Image(type="filepath", label="xray-classification")
+    ],
+    examples=[
+        ["samples/BacterialPneumonia/87.jpeg"],
+        ["samples/COVID-19/1.jpeg"],
+        ["samples/Normal/53.jpeg"],
+        ["samples/ViralPneumonia/106.jpeg"]
+    ],
+    description="upload or choose a sample image file",
+    outputs="text")
+
+tabs = gr.TabbedInterface (
+    [
+        xray_file_tab
+    ],
+    [
+        "Xray Classification"
+    ]
+)
+
+if __name__  == "__main__":
+    tabs.launch()

requirements.txt

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+torch
+torchvision
+torchaudio
+transformers
+fastai