app.py

from fastapi import FastAPI, UploadFile, File, Response, Request, Form, Body
from fastapi.staticfiles import StaticFiles
from fastapi.responses import FileResponse
import ggwave
import scipy.io.wavfile as wav
import numpy as np
import os
from pydantic import BaseModel
from groq import Groq
import io
import wave
import json
from typing import List, Dict, Optional


app = FastAPI()

# Serve static files
app.mount("/static", StaticFiles(directory="static"), name="static")

# Initialize Groq client
client = Groq(api_key=os.environ.get("GROQ_API_KEY"))

class TextInput(BaseModel):
    text: str

@app.get("/")
async def serve_homepage():
    """Serve the chat interface HTML."""
    return FileResponse("static/index.html")

@app.get("/conv/")
async def serve_convpage():
    """Serve the chat interface HTML."""
    return FileResponse("static/conv.html")

@app.post("/stt/")
async def speech_to_text(file: UploadFile = File(...)):
    """Convert WAV audio file to text using ggwave."""
    with open("temp.wav", "wb") as audio_file:
        audio_file.write(await file.read())
    
    # Load WAV file
    fs, recorded_waveform = wav.read("temp.wav")
    os.remove("temp.wav")
    
    # Convert to bytes and decode
    waveform_bytes = recorded_waveform.astype(np.uint8).tobytes()
    decoded_message = ggwave.decode(instance, waveform_bytes)
    
    return {"text": decoded_message}

@app.post("/tts/")
def text_to_speech(input_text: TextInput):
    """Convert text to a WAV audio file using ggwave and return as response."""
    encoded_waveform = ggwave.encode(input_text.text, protocolId=1, volume=100)
    
    # Convert byte data into float32 array
    waveform_float32 = np.frombuffer(encoded_waveform, dtype=np.float32)
    
    # Normalize float32 data to the range of int16
    waveform_int16 = np.int16(waveform_float32 * 32767)
    
    # Save to buffer instead of a file
    buffer = io.BytesIO()
    with wave.open(buffer, "wb") as wf:
        wf.setnchannels(1)                  # Mono audio
        wf.setsampwidth(2)                  # 2 bytes per sample (16-bit PCM)
        wf.setframerate(48000)              # Sample rate
        wf.writeframes(waveform_int16.tobytes())  # Write waveform as bytes
    
    buffer.seek(0)
    return Response(content=buffer.getvalue(), media_type="audio/wav")

@app.post("/chat/")
async def chat_with_llm(file: UploadFile = File(...)):
    """Process input WAV, send text to LLM, and return generated response as WAV."""
    try:
        # Log file details
        print(f"File received: {file.filename}, Content-Type: {file.content_type}")

        # Read the file content into memory
        file_content = await file.read()
        if not file_content:
            return Response(
                content="Empty file uploaded",
                media_type="text/plain",
                status_code=400
            )

        # Initialize ggwave instance
        instance = ggwave.init()

        # Create a BytesIO object to use with wav.read
        with io.BytesIO(file_content) as buffer:
            try:
                fs, recorded_waveform = wav.read(buffer)
                recorded_waveform = recorded_waveform.astype(np.float32) / 32767.0
                waveform_bytes = recorded_waveform.tobytes()
                user_message = ggwave.decode(instance, waveform_bytes)

                if user_message is None:
                    return Response(
                        content="No message detected in audio",
                        media_type="text/plain",
                        status_code=400
                    )

                print("Decoded user message:", user_message.decode("utf-8"))

                # Send to LLM
                chat_completion = client.chat.completions.create(
                    messages=[
                        {"role": "system", "content": "you are a helpful assistant. answer always in one sentence"},
                        {"role": "user", "content": user_message.decode("utf-8")}
                    ],
                    model="llama-3.3-70b-versatile",
                )

                llm_response = chat_completion.choices[0].message.content
                print("LLM Response:", llm_response)

                # Convert response to audio
                encoded_waveform = ggwave.encode(llm_response, protocolId=1, volume=100)

                # Convert byte data into float32 array
                waveform_float32 = np.frombuffer(encoded_waveform, dtype=np.float32)

                # Normalize float32 data to the range of int16
                waveform_int16 = np.int16(waveform_float32 * 32767)

                # Save to buffer instead of a file
                buffer = io.BytesIO()
                with wave.open(buffer, "wb") as wf:
                    wf.setnchannels(1)  # Mono audio
                    wf.setsampwidth(2)  # 2 bytes per sample (16-bit PCM)
                    wf.setframerate(48000)  # Sample rate
                    wf.writeframes(waveform_int16.tobytes())  # Write waveform as bytes

                buffer.seek(0)
                ggwave.free(instance)
                return Response(
                    content=buffer.getvalue(),
                    media_type="audio/wav",
                    headers={
                        "X-User-Message": user_message.decode("utf-8"),
                        "X-LLM-Response": llm_response
                    }
                )

            except Exception as e:
                print(f"Error processing audio: {str(e)}")
                ggwave.free(instance)
                return Response(
                    content=f"Error processing audio: {str(e)}",
                    media_type="text/plain",
                    status_code=500
                )

    except Exception as e:
        print(f"Unexpected error: {str(e)}")
        return Response(
            content=f"Unexpected error: {str(e)}",
            media_type="text/plain",
            status_code=500
        )
@app.post("/continuous-chat/")
async def continuous_chat(
    file: UploadFile = File(...),
    chat_history: Optional[str] = Form(None)
):
    """Process input WAV with chat history, send text to LLM, and return response as WAV."""
    # Initialize ggwave instance
    instance = ggwave.init()
    
    
    
    # Parse chat history if provided
    messages = [{"role": "system", "content": "you are a helpful assistant. answer always in one sentence"}]
    
    if chat_history:
        try:
            history = json.loads(chat_history)
            for msg in history:
                if msg["role"] in ["user", "assistant"]:
                    messages.append(msg)
        except Exception as e:
            print(f"Error parsing chat history: {str(e)}")
    
    # Read the file content into memory
    file_content = await file.read()
    
    # Process the audio file
    with io.BytesIO(file_content) as buffer:
        try:
            fs, recorded_waveform = wav.read(buffer)
            recorded_waveform = recorded_waveform.astype(np.float32) / 32767.0
            waveform_bytes = recorded_waveform.tobytes()
            user_message = ggwave.decode(instance, waveform_bytes)
            
            if user_message is None:
                return Response(
                    content="No message detected in audio",
                    media_type="text/plain",
                    status_code=400
                )
            
            decoded_message = user_message.decode("utf-8")
            print("user_message: " + decoded_message)
            
            # Add user message to messages
            messages.append({"role": "user", "content": decoded_message})
            
            # Send to LLM with full chat history
            chat_completion = client.chat.completions.create(
                messages=messages,
                model="llama-3.3-70b-versatile",
            )
            
            llm_response = chat_completion.choices[0].message.content
            print(llm_response)
            
            # Convert response to audio
            encoded_waveform = ggwave.encode(llm_response, protocolId=1, volume=100)
            waveform_float32 = np.frombuffer(encoded_waveform, dtype=np.float32)
            waveform_int16 = np.int16(waveform_float32 * 32767)
            
            # Save to buffer
            buffer = io.BytesIO()
            with wave.open(buffer, "wb") as wf:
                wf.setnchannels(1)
                wf.setsampwidth(2)
                wf.setframerate(48000)
                wf.writeframes(waveform_int16.tobytes())
            
            buffer.seek(0)
            ggwave.free(instance)
            
            return Response(
                content=buffer.getvalue(),
                media_type="audio/wav",
                headers={
                    "X-User-Message": decoded_message,
                    "X-LLM-Response": llm_response
                }
            )
            
        except Exception as e:
            print(f"Error processing audio: {str(e)}")
            ggwave.free(instance)
            return Response(
                content=f"Error processing audio: {str(e)}",
                media_type="text/plain",
                status_code=500
            )