Spaces:
Sleeping
Sleeping
File size: 2,565 Bytes
fe96ce9 fd097e2 fe96ce9 fd097e2 fe96ce9 e87a452 fd097e2 e87a452 fe96ce9 e87a452 fe96ce9 e87a452 fe96ce9 e87a452 fe96ce9 e87a452 fe96ce9 e87a452 fe96ce9 e87a452 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 |
# main.py
import gradio as gr
from gradio.themes import Soft
from question_runner import run_tool
from config import MODEL_PRIORITY, SYNTAX_DOC_URL, MORPHOLOGY_DOC_URL, SEMANTICS_DOC_URL
from doc_utils import get_questions_from_doc
# Estimate runtime based on # of questions
def estimate_runtime(passage, doc_type):
if not passage or not doc_type:
return ""
if doc_type.lower() == "syntax":
doc_url = SYNTAX_DOC_URL
elif doc_type.lower() == "morphology":
doc_url = MORPHOLOGY_DOC_URL
else: # semantics
doc_url = SEMANTICS_DOC_URL
questions = get_questions_from_doc(doc_url)
if not questions or (isinstance(questions, list) and questions and str(questions[0]).startswith("Error")):
return "Unable to load questions."
est_seconds = round(len(questions) * 2.5, 1)
return f"Estimated generation time: ~{est_seconds} seconds"
# Build the Gradio interface at module level (required for HF Spaces)
with gr.Blocks(theme=Soft()) as demo:
gr.Markdown("""
## **Classical Language Query Assistant**
Submit a Latin or Greek passage and select the question type.
Answers are generated using a rotating chain of hosted AI models via OpenRouter.
""")
with gr.Row():
passage_input = gr.Textbox(label="Latin or Greek Passage", lines=4)
question_type = gr.Radio(["Syntax", "Morphology", "Semantics"], label="Question Type")
top_model = MODEL_PRIORITY[0]
full_model_list = "\n".join(f"- `{m}`" for m in MODEL_PRIORITY)
gr.Markdown(f"""
**Currently prioritized model:** `{top_model}`
**Model fallback chain (if needed):**
{full_model_list}
""")
estimated_time_box = gr.Textbox(label="Estimated Time", interactive=False)
with gr.Row():
output_text = gr.Textbox(label="Generated Answers", lines=25, interactive=False)
output_file = gr.File(label="Download Answers (.txt)", interactive=False)
passage_input.change(fn=estimate_runtime, inputs=[passage_input, question_type], outputs=estimated_time_box)
question_type.change(fn=estimate_runtime, inputs=[passage_input, question_type], outputs=estimated_time_box)
submit_button = gr.Button("Generate Answers")
# Connect the button to run_tool function directly
submit_button.click(
fn=run_tool,
inputs=[passage_input, question_type],
outputs=[output_text, output_file, estimated_time_box],
api_name="generate"
)
if __name__ == "__main__":
demo.launch()
|