Add application file

app.py

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+import pandas as pd 
+import gradio as gr
+from io import StringIO
+
+
+# Creating a sample dataframe
+df = pd.DataFrame({
+    "Rank" : [1, 2, 3, 4, 5], 
+    "Detector" : ["""Roberta Detector\n(Numo et al.)\n<a href='https://www.google.de' style="color:blue">Google</a>""", """LLM Detector
+*(Numo et al.)*  
+<span style="color:blue">[Google](https://www.google.de)</span>
+""", "", "", ""], 
+    "All Gen. [F1]" : [20, 20, 7, 8, 7], 
+    "Llama-2 7B Gen. [F1]" : [14, 3, 6, 2, 6], 
+    "GPT-4 Gen. [F1]" : [23, 45, 2, 32, 2]
+}) 
+
+
+def process_csv_text(temp_file):
+    if isinstance(temp_file, str):
+      df = pd.read_csv(StringIO(temp_file), parse_dates=["Start date", "End date"])
+    else:
+      df = pd.read_csv(temp_file.name, parse_dates=["Start date", "End date"])
+    print(df)
+    return df
+  
+numeric_cols = df.select_dtypes(include='number').columns
+traget_cols = ['All Gen. [F1]', 'Llama-2 7B Gen. [F1]', 'GPT-4 Gen. [F1]']
+
+# Applying style to highlight the maximum value in each row
+#styler = df.style.highlight_max(color = 'lightgreen', axis = 0)
+styler = df.style.set_properties(**{'text-align': 'center'},subset=['Detector'])\
+                  .highlight_max(color='lightgreen', axis=0, subset=traget_cols)
+title = "Test"
+desc = "Test gradio"
+# Displaying the styled dataframe in Gradio
+with gr.Blocks() as demo:
+    
+
+    gr.Markdown(
+    """
+    # <center>Hello World!</center>
+    <center>Start typing below to see the output.</center>
+    """)
+    gr.DataFrame(styler, line_breaks=True, datatype="markdown")
+
+
+    # Upload table
+    gr.Markdown(
+    """
+    # <center>Hello World!</center>
+    <center>Start typing below to see the output.</center>
+    """)
+  
+    upload_button = gr.UploadButton(label="Upload Predictions", file_types = ['.csv'], live=True, file_count = "single")    
+    table = gr.Dataframe(headers=["Start date", "End date"], type="pandas", col_count=2)
+
+    
+    upload_button.upload(fn=process_csv_text, inputs=upload_button, outputs=table, api_name="upload_csv")
+
+    
+demo.launch()