app.py

import gradio as gr
import os
import time
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import EmbeddingRetriever
import pandas as pd


def load_qa_model():
  document_store = InMemoryDocumentStore()
  retriever = EmbeddingRetriever(
    document_store=document_store,
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    use_gpu=False,
    scale_score=False,
  )
  # Get dataframe with columns "question", "answer" and some custom metadata
  df = pd.read_csv('social-faq.csv', on_bad_lines='skip', delimiter=';')
  # Minimal cleaning
  df.fillna(value="", inplace=True)
  df["question"] = df["question"].apply(lambda x: x.strip())

  questions = list(df["question"].values)
  df["embedding"] = retriever.embed_queries(queries=questions).tolist()
  df = df.rename(columns={"question": "content"})

  # Convert Dataframe to list of dicts and index them in our DocumentStore
  docs_to_index = df.to_dict(orient="records")
  document_store.write_documents(docs_to_index)

  return retriever

def add_text(history, text):
    history = history + [(text, None)]
    return history, gr.Textbox(value="", interactive=False)


def add_file(history, file):
    history = history + [((file.name,), None)]
    return history


def bot(history):
    print(history)
    # response = "**That's cool!**"
    history[-1][1] = ""

    global retriever
    response = get_answers(retriever, history[0][0])

    for character in response:
      history[-1][1] += character
      time.sleep(0.01)
      yield history



def get_answers(retriever, query):
  from haystack.pipelines import FAQPipeline

  pipe = FAQPipeline(retriever=retriever)

  from haystack.utils import print_answers

  # Run any question and change top_k to see more or less answers
  prediction = pipe.run(query=query, params={"Retriever": {"top_k": 1}})

  answers = prediction['answers']

  if answers:
    return answers[0].answer
  else:
    return "I don't have an answer to that question"




retriever = load_qa_model()


with gr.Blocks() as demo:
    chatbot = gr.Chatbot(
        [],
        elem_id="chatbot",
        bubble_full_width=False,
        # avatar_images=(None, "/content/avatar.png"),
    )

    with gr.Row():
        txt = gr.Textbox(
            scale=4,
            show_label=False,
            placeholder="Enter text and press enter",
            container=False,
        )
        inputRecord = gr.Audio(label="Record a question", source="microphone", type="filepath")
        audioOutput = gr.Audio(label="Listen the answer", interactive=False)

    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
        bot, chatbot, chatbot
    )
    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)

demo.queue()
demo.launch()