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import gradio as gr
import os
from ui.dataset_input import create_dataset_input, load_example_dataset
from ui.analysis_screen import process_analysis_request
def create_app():
"""
Create a streamlined Gradio app for dataset input and Bag of Words analysis.
Returns:
gr.Blocks: The Gradio application
"""
with gr.Blocks(title="LLM Response Comparator") as app:
# Application state to share data between tabs
dataset_state = gr.State({})
analysis_results_state = gr.State({})
# Dataset Input Tab
with gr.Tab("Dataset Input"):
dataset_inputs, example_dropdown, load_example_btn, create_btn, prompt, response1, model1, response2, model2 = create_dataset_input()
# Load example dataset
load_example_btn.click(
fn=load_example_dataset,
inputs=[example_dropdown],
outputs=[dataset_inputs] # Ensure `load_example_dataset` returns compatible data
)
# Save dataset to state
create_btn.click(
fn=lambda p, r1, m1, r2, m2: {
"entries": [
{"prompt": p, "response": r1, "model": m1},
{"prompt": p, "response": r2, "model": m2}
]
},
inputs=[prompt, response1, model1, response2, model2], # Ensure these are valid Gradio components
outputs=[dataset_state] # Ensure `dataset_state` is correctly defined
)
# Analysis Tab
with gr.Tab("Analysis"):
analysis_options = gr.CheckboxGroup(
choices=["Bag of Words"],
value=["Bag of Words"],
label="Select Analyses to Run"
)
run_analysis_btn = gr.Button("Run Analysis", variant="primary")
analysis_output = gr.JSON(label="Analysis Results", visible=False)
# Run analysis
run_analysis_btn.click(
fn=process_analysis_request,
inputs=[dataset_state, analysis_options], # Removed None
outputs=[analysis_results_state, analysis_output]
)
return app
if __name__ == "__main__":
# Create and launch the app
app = create_app()
app.launch() |