app.py

import asyncio
import base64
import json
import os
import pathlib
from typing import AsyncGenerator, Literal, List

import numpy as np
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import HTMLResponse
from fastrtc import AsyncStreamHandler, Stream, get_twilio_turn_credentials, wait_for_item
from pydantic import BaseModel
import uvicorn

# --- Import get_space to detect Hugging Face Spaces ---
from gradio.utils import get_space

# --- Document processing and RAG libraries ---
import PyPDF2
import docx
import faiss
from sentence_transformers import SentenceTransformer
from transformers import pipeline

# --- Speech processing libraries ---
import whisper
from gtts import gTTS
from pydub import AudioSegment
import io

# Load environment variables and define current directory
load_dotenv()
current_dir = pathlib.Path(__file__).parent

# ====================================================
# 1. Document Ingestion & RAG Pipeline Setup
# ====================================================

# Folder containing PDFs, Word docs, and text files (place this folder alongside app.py)
DOCS_FOLDER = current_dir / "docs"

def extract_text_from_pdf(file_path: pathlib.Path) -> str:
    text = ""
    with open(file_path, "rb") as f:
        reader = PyPDF2.PdfReader(f)
        for page in reader.pages:
            page_text = page.extract_text()
            if page_text:
                text += page_text + "\n"
    return text

def extract_text_from_docx(file_path: pathlib.Path) -> str:
    doc = docx.Document(file_path)
    return "\n".join([para.text for para in doc.paragraphs])

def extract_text_from_txt(file_path: pathlib.Path) -> str:
    with open(file_path, "r", encoding="utf-8") as f:
        return f.read()

def load_documents(folder: pathlib.Path) -> List[str]:
    documents = []
    for file_path in folder.glob("*"):
        if file_path.suffix.lower() == ".pdf":
            documents.append(extract_text_from_pdf(file_path))
        elif file_path.suffix.lower() in [".docx", ".doc"]:
            documents.append(extract_text_from_docx(file_path))
        elif file_path.suffix.lower() == ".txt":
            documents.append(extract_text_from_txt(file_path))
    return documents

def split_text(text: str, max_length: int = 500, overlap: int = 100) -> List[str]:
    chunks = []
    start = 0
    while start < len(text):
        end = start + max_length
        chunks.append(text[start:end])
        start += max_length - overlap
    return chunks

# Load and process documents
documents = load_documents(DOCS_FOLDER)
all_chunks = []
for doc in documents:
    all_chunks.extend(split_text(doc))

# Compute embeddings and build FAISS index
embedding_model = SentenceTransformer("all-MiniLM-L6-v2")
chunk_embeddings = embedding_model.encode(all_chunks)
embedding_dim = chunk_embeddings.shape[1]
index = faiss.IndexFlatL2(embedding_dim)
index.add(np.array(chunk_embeddings))

# Setup a text-generation pipeline (using GPT-2 here as an example)
generator = pipeline("text-generation", model="gpt2", max_length=256)

def retrieve_context(query: str, k: int = 5) -> List[str]:
    query_embedding = embedding_model.encode([query])
    distances, indices = index.search(np.array(query_embedding), k)
    return [all_chunks[idx] for idx in indices[0] if idx < len(all_chunks)]

def generate_answer(query: str) -> str:
    context_chunks = retrieve_context(query)
    context = "\n".join(context_chunks)
    prompt = (
        f"You are a customer support agent. Use the following context to answer the question.\n\n"
        f"Context:\n{context}\n\n"
        f"Question: {query}\n\n"
        f"Answer:"
    )
    response = generator(prompt, max_length=256, do_sample=True, temperature=0.7)
    return response[0]["generated_text"]

# ====================================================
# 2. Speech-to-Text and Text-to-Speech Functions
# ====================================================

# Load Whisper model for speech-to-text
stt_model = whisper.load_model("base", device="cpu")

def speech_to_text(audio_array: np.ndarray, sample_rate: int = 16000) -> str:
    # Convert int16 PCM to float32 normalized to [-1, 1]
    audio_float = audio_array.astype(np.float32) / 32768.0
    result = stt_model.transcribe(audio_float, fp16=False)
    return result["text"]

def text_to_speech(text: str, lang="en", target_sample_rate: int = 24000) -> np.ndarray:
    tts = gTTS(text, lang=lang)
    mp3_fp = io.BytesIO()
    tts.write_to_fp(mp3_fp)
    mp3_fp.seek(0)
    audio = AudioSegment.from_file(mp3_fp, format="mp3")
    audio = audio.set_frame_rate(target_sample_rate).set_channels(1)
    return np.array(audio.get_array_of_samples(), dtype=np.int16)

# ====================================================
# 3. RAGVoiceHandler: Integrating Voice & RAG
# ====================================================

class RAGVoiceHandler(AsyncStreamHandler):
    def __init__(
        self,
        expected_layout: Literal["mono"] = "mono",
        output_sample_rate: int = 24000,
        output_frame_size: int = 480,
    ) -> None:
        super().__init__(
            expected_layout,
            output_sample_rate,
            output_frame_size,
            input_sample_rate=16000,
        )
        self.input_queue: asyncio.Queue = asyncio.Queue()
        self.output_queue: asyncio.Queue = asyncio.Queue()
        self.quit: asyncio.Event = asyncio.Event()
        self.input_buffer = bytearray()
        self.last_input_time = asyncio.get_event_loop().time()

    def copy(self) -> "RAGVoiceHandler":
        # Return a new instance with the same configuration
        return RAGVoiceHandler(
            expected_layout="mono",
            output_sample_rate=self.output_sample_rate,
            output_frame_size=self.output_frame_size,
        )

    async def stream(self) -> AsyncGenerator[bytes, None]:
        # Continuously check for new audio; if a short silence occurs (timeout), process the buffered utterance.
        while not self.quit.is_set():
            try:
                audio_data = await asyncio.wait_for(self.input_queue.get(), timeout=0.5)
                self.input_buffer.extend(audio_data)
                self.last_input_time = asyncio.get_event_loop().time()
            except asyncio.TimeoutError:
                if self.input_buffer:
                    # Process the buffered utterance
                    audio_array = np.frombuffer(self.input_buffer, dtype=np.int16)
                    self.input_buffer = bytearray()
                    query_text = speech_to_text(audio_array, sample_rate=self.input_sample_rate)
                    if query_text.strip():
                        print("Transcribed query:", query_text)
                        answer_text = generate_answer(query_text)
                        print("Generated answer:", answer_text)
                        tts_audio = text_to_speech(answer_text, target_sample_rate=self.output_sample_rate)
                        self.output_queue.put_nowait((self.output_sample_rate, tts_audio))
            await asyncio.sleep(0.1)

    async def receive(self, frame: tuple[int, np.ndarray]) -> None:
        # Each received frame is added as bytes to the input queue
        sample_rate, audio_array = frame
        audio_bytes = audio_array.tobytes()
        await self.input_queue.put(audio_bytes)

    async def emit(self) -> tuple[int, np.ndarray] | None:
        return await wait_for_item(self.output_queue)

    def shutdown(self) -> None:
        self.quit.set()

# ====================================================
# 4. Voice Streaming Setup & FastAPI Endpoints
# ====================================================

# When running on Hugging Face Spaces, supply a dummy RTC configuration.
if get_space():
    rtc_config = {"iceServers": [{"urls": "stun:stun.l.google.com:19302"}]}
else:
    rtc_config = get_twilio_turn_credentials()

# Create a Stream instance using our RAGVoiceHandler.
stream = Stream(
    modality="audio",
    mode="send-receive",
    handler=RAGVoiceHandler(),
    rtc_configuration=rtc_config,
    concurrency_limit=5,
    time_limit=90,
)

# Define a simple input hook (if needed by the client to initialize the call)
class InputData(BaseModel):
    webrtc_id: str

app = FastAPI()
stream.mount(app)

@app.post("/input_hook")
async def input_hook(body: InputData):
    stream.set_input(body.webrtc_id)
    return {"status": "ok"}

# Endpoint to handle WebRTC offer from the client (for voice calls)
@app.post("/webrtc/offer")
async def webrtc_offer(offer: dict):
    # This uses fastrtc's built-in handling of the offer to set up the connection.
    return await stream.handle_offer(offer)

# Serve your existing HTML file (which contains your voice UI)
@app.get("/")
async def index():
    index_path = current_dir / "index.html"
    html_content = index_path.read_text()
    return HTMLResponse(content=html_content)

# ====================================================
# 5. Application Runner
# ====================================================

if __name__ == "__main__":
    mode = os.getenv("MODE", "PHONE")
    if mode == "UI":
        # Optionally launch a text-based Gradio interface for testing the RAG backend
        import gradio as gr
        def gradio_chat(user_input):
            return generate_answer(user_input)
        iface = gr.Interface(fn=gradio_chat, inputs="text", outputs="text", title="Customer Support Chatbot")
        iface.launch(server_port=7860)
    elif mode == "PHONE":
        # Run the FastAPI app so that callers can use the voice functionality.
        uvicorn.run(app, host="0.0.0.0", port=7860)
    else:
        uvicorn.run(app, host="0.0.0.0", port=7860)