Create app.py

app.py

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+import time 
+import gradio as gr
+import pandas as pd
+
+
+def load_and_analyze_csv(file, text_field):
+    df = pd.read_csv(file.name)
+    
+    if text_field not in df.columns:
+        raise gr.Error(f"Error: Enter text column'{text_field}' not in CSV file.")
+    
+    fire_related = gr.CheckboxGroup(choices=df['text'].to_list()[:5])
+    flood_related = gr.CheckboxGroup(choices=df['text'].to_list()[:7])
+    not_related = gr.CheckboxGroup(choices=df['text'].to_list())
+    time.sleep(5)
+    return fire_related, flood_related, not_related
+
+def analyze_selected_texts(selections):
+    selected_texts = selections
+    
+    analysis_results = [f"Word Count: {len(text.split())}" for text in selected_texts]
+    
+    result_df = pd.DataFrame({"Selected Text": selected_texts, "Analysis": analysis_results})
+    return result_df
+
+
+with gr.Blocks() as demo:
+    event_models = ["jayebaku/distilbert-base-multilingual-cased-crexdata-relevance-classifier"]
+    
+    with gr.Tab("Event Type Classification"):
+        with gr.Row(equal_height=True):
+            with gr.Column(scale=4):
+                file_input = gr.File(label="Upload CSV File")
+                
+            with gr.Column(scale=6):
+                text_field = gr.Textbox(label="Text field name", value="text")
+                event_model = gr.Dropdown(event_models, label="Select classification model")
+                predict_button = gr.Button("Start Prediction")
+        
+        with gr.Row(): # XXX confirm this is not a problem later --equal_height=True
+            with gr.Column():
+                gr.Markdown("""### Flood-related""")
+                fire_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications")
+                
+            with gr.Column():
+                gr.Markdown("""### Fire-related""")
+                flood_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications")
+                
+            with gr.Column():
+                gr.Markdown("""### None""")
+                none_checkbox_output = gr.CheckboxGroup(label="Select ONLY incorrect classifications") 
+                
+            predict_button.click(load_and_analyze_csv, inputs=[file_input, text_field], outputs=[fire_checkbox_output, flood_checkbox_output, none_checkbox_output])           
+                
+    with gr.Tab("Question Answering"):
+        # XXX Add some button disabling here, if the classification process is not completed first XXX
+        
+        analysis_button = gr.Button("Analyze Selected Texts")
+        analysis_output = gr.DataFrame(headers=["Selected Text", "Analysis"])
+        analysis_button.click(analyze_selected_texts, inputs=fire_checkbox_output, outputs=analysis_output)
+
+demo.launch()