app.py

from indexer import load_reddit_posts, create_vector_db, create_qa_chain
import gradio as gr


# Gradio interface function
def answer_question(subreddit, num_posts, category, question):
    docs = load_reddit_posts(subreddit, int(num_posts), category)
    db = create_vector_db(docs)
    qa_chain = create_qa_chain(db)
    result = qa_chain(question)
    output = result["result"]

    # Remove instruction sentence
    output = output.replace(
        "Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.",
        "",
    )
    # Remove everything after "Example:"
    output = output.split("Example:")[0]
    
    return output.strip()


# Gradio UI
demo = gr.Interface(
    fn=answer_question,
    inputs=[
        gr.Textbox(label="Subreddit (e.g. MachineLearning)"),
        gr.Slider(minimum=1, maximum=5, value=1, label="Number of Reddit Posts"),
        gr.Textbox(label="Category (e.g. hot, new, etc)"),
        gr.Textbox(label="Your Question"),
    ],
    outputs="text",
    title="Reddit RAG Assistant",
    description="Ask questions based on recent Reddit posts in a subreddit.",
)

demo.launch()