app.py

import gradio as gr
import torch
import requests
import tempfile
import threading
import numpy as np
from transformers import pipeline, AutoModelForSpeechSeq2Seq, AutoProcessor
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import Neo4jVector
from langchain_community.graphs import Neo4jGraph
from langchain_core.prompts import ChatPromptTemplate
import time
import os
import io
from pydub import AudioSegment
from dataclasses import dataclass
from utils import determine_pause

# Define AppState dataclass for managing the application's state
@dataclass
class AppState:
    stream: np.ndarray | None = None
    sampling_rate: int = 0
    pause_detected: bool = False
    stopped: bool = False
    conversation: list = []

# Neo4j setup
graph = Neo4jGraph(
    url="neo4j+s://c62d0d35.databases.neo4j.io",
    username="neo4j",
    password="_x8f-_aAQvs2NB0x6s0ZHSh3W_y-HrENDbgStvsUCM0"
)

# Initialize the vector index with Neo4j
vector_index = Neo4jVector.from_existing_graph(
    OpenAIEmbeddings(api_key=os.environ['OPENAI_API_KEY']),
    graph=graph,
    search_type="hybrid",
    node_label="Document",
    text_node_properties=["text"],
    embedding_node_property="embedding",
)

# Define the ASR model with Whisper
model_id = 'openai/whisper-large-v3'
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
processor = AutoProcessor.from_pretrained(model_id)

pipe_asr = pipeline(
    "automatic-speech-recognition",
    model=model,
    tokenizer=processor.tokenizer,
    feature_extractor=processor.feature_extractor,
    max_new_tokens=128,
    chunk_length_s=15,
    batch_size=16,
    torch_dtype=torch_dtype,
    device=device,
    return_timestamps=True
)

# Function to reset the state after 2 seconds
def auto_reset_state():
    time.sleep(2)
    return AppState()  # Reset the state

# Function to process audio input and transcribe it
def transcribe_function(state: AppState, new_chunk):
    try:
        sr, y = new_chunk[0], new_chunk[1]
    except TypeError:
        print(f"Error chunk structure: {type(new_chunk)}, content: {new_chunk}")
        return state, ""

    if y is None or len(y) == 0:
        return state, ""

    y = y.astype(np.float32)
    max_abs_y = np.max(np.abs(y))
    if max_abs_y > 0:
        y = y / max_abs_y

    if state.stream is not None and len(state.stream) > 0:
        state.stream = np.concatenate([state.stream, y])
    else:
        state.stream = y

    result = pipe_asr({"array": state.stream, "sampling_rate": sr}, return_timestamps=False)
    full_text = result.get("text", "")

    threading.Thread(target=auto_reset_state).start()
    return state, full_text

# Function to generate a response using the prompt and the context
def generate_response_with_prompt(context, question):
    formatted_prompt = prompt.format(context=context, question=question)
    llm = ChatOpenAI(temperature=0, api_key=os.environ['OPENAI_API_KEY'])
    response = llm(formatted_prompt)
    return response.content.strip()

# Function to generate audio with Eleven Labs TTS
def generate_audio_elevenlabs(text):
    XI_API_KEY = os.environ['ELEVENLABS_API']
    VOICE_ID = 'ehbJzYLQFpwbJmGkqbnW'
    tts_url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}/stream"
    headers = {"Accept": "application/json", "xi-api-key": XI_API_KEY}
    data = {"text": text, "model_id": "eleven_multilingual_v2", "voice_settings": {"stability": 1.0}}
    response = requests.post(tts_url, headers=headers, json=data, stream=True)
    if response.ok:
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as f:
            for chunk in response.iter_content(chunk_size=1024):
                f.write(chunk)
            return f.name
    else:
        print(f"Error generating audio: {response.text}")
        return None

# Define the function to retrieve information using Neo4j and the vector store
def retriever(question: str):
    structured_query = """
    CALL db.index.fulltext.queryNodes('entity', $query, {limit: 2})
    YIELD node, score
    RETURN node.id AS entity, node.text AS context, score
    ORDER BY score DESC
    LIMIT 2
    """
    structured_data = graph.query(structured_query, {"query": generate_full_text_query(question)})
    structured_response = "\n".join([f"{record['entity']}: {record['context']}" for record in structured_data])

    unstructured_data = [el.page_content for el in vector_index.similarity_search(question)]
    unstructured_response = "\n".join(unstructured_data)

    combined_context = f"Structured data:\n{structured_response}\n\nUnstructured data:\n{unstructured_response}"
    return generate_response_with_prompt(combined_context, question)

# Function to handle the entire audio query and response process
def process_audio_query(state: AppState, audio_input):
    state, transcription = transcribe_function(state, audio_input)
    response_text = retriever(transcription)
    audio_path = generate_audio_elevenlabs(response_text)
    return audio_path, state

# Create Gradio interface for audio input and output
with gr.Blocks() as interface:
    audio_input = gr.Audio(sources="microphone", type="numpy", streaming=True, every=0.1)
    submit_button = gr.Button("Submit")
    audio_output = gr.Audio(type="filepath", autoplay=True)
    state = gr.State(AppState())

    submit_button.click(fn=process_audio_query, inputs=[state, audio_input], outputs=[audio_output, state])

# Launch the Gradio app
interface.launch()