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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('/content/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()
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