interaction.py

from gtts import gTTS
import base64
import os

from haystack import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders import PromptBuilder
from haystack.components.generators.hugging_face_local import HuggingFaceLocalGenerator
from haystack.pipeline import Pipeline

def init_doc_store(path, files):
    docs = []
    for file in files:
        with open(path + '/' + file, 'r') as f:
            content = f.read()
            docs.append(Document(content=content, meta={'name':file}))

    document_store = InMemoryDocumentStore()
    document_store.write_documents(docs)
    return document_store

def define_components(document_store):
    retriever = InMemoryBM25Retriever(document_store, top_k=3)
    
    template = """
    Given the following information, answer the question.
    
    Context:
    {% for document in documents %}
        {{ document.content }}
    {% endfor %}
    
    Question: {{question}}
    Answer:
    """
    prompt_builder = PromptBuilder(template=template)
    
    generator = HuggingFaceLocalGenerator(model="gpt2",
                                          task="text-generation",
                                        #   device='cuda',
                                          generation_kwargs={
                                            "max_new_tokens": 100,
                                            "temperature": 0.9,
                                            })
    generator.warm_up()
    return retreiver, prompt_builder, generator

def define_pipeline(retreiver, prompt_builder, generator):
    basic_rag_pipeline = Pipeline()
    
    basic_rag_pipeline.add_component("retriever", retriever)
    basic_rag_pipeline.add_component("prompt_builder", prompt_builder)
    basic_rag_pipeline.add_component("llm", generator)
    
    basic_rag_pipeline.connect("retriever", "prompt_builder.documents")
    basic_rag_pipeline.connect("prompt_builder", "llm")

    return basic_rag_pipeline

def generate_response(question, pipeline):
	response = pipeline.run({'retriever':{"query":question}, 'prompt_builder':{'question':question}})
    response = response['llm']['replies'][0]
    return response
def audio_response(response):
	audio_stream="response_audio.mp3"
	tts = gTTS(response)
	tts.save(audio_stream)
	with open(audio_stream, 'rb') as file:
		audio_data = file.read()
	audio_base64 = base64.b64encode(audio_data).decode('utf-8')
	audio_tag = f'<audio autoplay="true" src="data:audio/mp3;base64,{audio_base64}">'
	return audio_tag