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from pathlib import Path
import gradio as gr
from langchain import FAISS
from langchain.embeddings import OpenAIEmbeddings
import os
embeddings = OpenAIEmbeddings()
faiss_path = Path(__file__).parent / "faiss_index_03"
docsearch = FAISS.load_local(faiss_path, embeddings)
def generate_outputs(query: str, k: int):
outputs = []
docs_and_scores = docsearch.similarity_search_with_score(query, k=k)
for doc, score in docs_and_scores:
output_text = f"{doc.page_content} + \n Score: {score:.2f}"
outputs.append(output_text)
return "\n---------------------------------------------------------\n" \
"---------------------------------------------------------\n" \
"---------------------------------------------------------\n".join(outputs)
# Define input/output interfaces and options
iface = gr.Interface(
fn=generate_outputs,
inputs=[
gr.Textbox(label="Enter your text", value="Sample text", lines=2),
gr.Slider(label="Number of outputs", minimum=1, maximum=12, value=4)
],
outputs=[
gr.Textbox(label="Generated Outputs")
],
title="Text Generation App",
description="Enter your text and choose the number of outputs you'd like to generate"
)
# Launch the Gradio app
iface.launch() |