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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() | |