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import gradio as gr
from src.rag.pipeline import answer_question
def format_answer(question: str) -> tuple[str, str]:
"""Format answer with citations."""
answer, citations = answer_question(question)
# Format citations
if citations:
citations_text = "\n\n### 📚 Sources:\n"
for i, citation in enumerate(citations, 1):
citations_text += f"{i}. **{citation['title']}** ({citation['year']})\n"
else:
citations_text = ""
return answer, citations_text
def run_gradio():
with gr.Blocks(title="PaperMate", theme=gr.themes.Soft()) as demo:
gr.Markdown(
"""
# 📄 PaperMate — Research Paper Q&A Assistant
Ask questions about research papers and get answers backed by scientific literature.
"""
)
with gr.Row():
with gr.Column():
question = gr.Textbox(
label="Your Question",
placeholder="e.g., What techniques are used to handle out-of-vocabulary words in NLP?",
lines=3,
)
btn = gr.Button("🔍 Search & Answer", variant="primary")
with gr.Row():
with gr.Column():
output = gr.Textbox(label="Answer", lines=10, max_lines=20)
citations = gr.Markdown(label="Sources")
btn.click(fn=format_answer, inputs=question, outputs=[output, citations])
gr.Markdown(
"""
---
💡 **Tip:** Questions are answered using relevant papers from the ArXiv dataset.
"""
)
demo.launch()
if __name__ == "__main__":
run_gradio()
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