app.py

app.py

@@ -0,0 +1,39 @@
+from transformers import pipeline
+import gradio as gr
+
+
+modelName = "Brain-Tumor-Classification"
+hfUser = "Hemg"
+
+
+def prediction_function(inputFile):
+    # get user name of their hugging face
+    modelPath = hfUser + "/" + modelName
+    # takes some time
+    classifier = pipeline("image-classification", model=modelPath)
+
+    try:
+        result = classifier(inputFile)
+        predictions = dict()
+        labels = []
+        for eachLabel in result:
+            predictions[eachLabel["label"]] = eachLabel["score"]
+            labels.append(eachLabel["label"])
+        result = predictions
+    except:
+        result = "no data provided!!"
+
+    return result
+
+
+# change modelName parameter
+def create_demo():
+    demo = gr.Interface(
+        fn=prediction_function,
+        inputs=gr.Image(type="pil"),
+        outputs=gr.Label(num_top_classes=3),
+    )
+    demo.launch()
+
+
+create_demo()