NimaKL commited on
Commit
ca583df
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1 Parent(s): c7a36be

Update app.py

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Files changed (1) hide show
  1. app.py +19 -3
app.py CHANGED
@@ -1,4 +1,5 @@
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  import gradio as gr
 
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  from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
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  model_name = "NimaKL/FireWatch_tiny_75k"
@@ -12,6 +13,17 @@ def predict(text):
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  label_id = logits.argmax(axis=1).item()
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  return "Danger of fire hazard!" if label_id == 1 else "It is unlikely that a fire will start in this area."
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  io = gr.Interface(
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  fn=predict,
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  inputs="text",
@@ -19,10 +31,14 @@ io = gr.Interface(
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  title="FireWatch",
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  description="Predict whether a data row describes a fire hazard or not.",
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  output_description="Prediction",
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- 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']]
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- ,
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  output_component_names=["Prediction"],
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- theme="Streamlit"
 
 
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  )
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  io.launch()
 
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  import gradio as gr
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+ from IPython.display import IFrame
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  from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
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  model_name = "NimaKL/FireWatch_tiny_75k"
 
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  label_id = logits.argmax(axis=1).item()
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  return "Danger of fire hazard!" if label_id == 1 else "It is unlikely that a fire will start in this area."
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+ # Define a custom CSS style
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+ custom_style = """
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+ body {
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+ background-color: #F262626;
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+ }
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+ """
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+
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+ # Define the function to display the Google Sheets document
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+ def show_sheet():
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+ return IFrame("https://docs.google.com/spreadsheets/d/1b2aAZ8ue5NI7hZ-MiNc8y4dFthbrRiXlsaEeYSHgNCM/edit?usp=sharing", width=800, height=600)
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+
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  io = gr.Interface(
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  fn=predict,
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  inputs="text",
 
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  title="FireWatch",
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  description="Predict whether a data row describes a fire hazard or not.",
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  output_description="Prediction",
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+ 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']],
 
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  output_component_names=["Prediction"],
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+ theme="Streamlit",
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+ css=custom_style,
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+ article="<h2>FireWatch App</h2><p>This app predicts whether a data row describes a fire hazard or not. The prediction is based on a machine learning model that has been trained on a dataset of data rows.</p><h2>Data Rows Sheet</h2>"
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  )
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+ # Add the IFrame component to the app
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+ io.add_component("Sample Data Sheet", gr.outputs.HTML, show_sheet)
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+
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  io.launch()