Upload app-gradle.py

app-gradle.py

@@ -0,0 +1,28 @@
+import gradio as gr
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+model_name = "NimaKL/FireWatch_tiny_75k"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def predict(text):
+    inputs = tokenizer(text, return_tensors="pt")
+    outputs = model(**inputs)
+    logits = outputs.logits
+    label_id = logits.argmax(axis=1).item()
+    return "Danger of fire hazard!" if label_id == 1 else "It is unlikely that a fire will start in this area."
+
+io = gr.Interface(
+    fn=predict,
+    inputs="text",
+    outputs="text",
+    title="FireWatch",
+    description="Predict whether a data row describes a fire hazard or not.",
+    output_description="Prediction",
+    examples=[['-26.76123, 147.15512, 393.02, 203.63'], ['-26.7598, 147.14514, 361.54, 79.4'], ['-25.70059, 149.48932, 313.9, 5.15'], ['-24.4318, 151.83102, 307.98, 8.79'], ['-23.21878, 148.91298, 314.08, 7.4'], ['7.87518, 19.9241, 316.32, 39.63'], ['-20.10942, 148.14326, 314.39, 8.8'], ['7.87772, 19.9048, 304.14, 13.43'], ['-20.79866, 124.46834, 366.74, 89.06']]
+,
+    output_component_names=["Prediction"],
+    theme="Streamlit"
+)
+
+io.launch()