app.py

# from fastapi import FastAPI, Response
# from fastapi.responses import FileResponse
# from kokoro import KPipeline
# import soundfile as sf
# import os
# import numpy as np
# import torch 
# from huggingface_hub import InferenceClient

# def llm_chat_response(text):
#     HF_TOKEN = os.getenv("HF_TOKEN")
#     client = InferenceClient(
#     provider="hf-inference",
#     api_key=HF_TOKEN,)

#     response_from_llama = client.chat.completions.create(
#         model="meta-llama/Llama-3.2-11B-Vision-Instruct",
#         messages=[
#             {
#                 "role": "user",
#                 "content": [
#                     {
#                         "type": "text",
#                         "text": "Describe this image in one sentence."
#                     }#,
#                     # {
#                     #     "type": "image_url",
#                     #     "image_url": {
#                     #         "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
#                     #     }
#                     # }
#                 ]
#             }
#         ],
#         max_tokens=500,
#     )
   
    

#     return response_from_llama.choices[0].message['content']

# app = FastAPI()

# # Initialize pipeline once at startup
# pipeline = KPipeline(lang_code='a')

# @app.post("/generate")
# async def generate_audio(text: str, voice: str = "af_heart", speed: float = 1.0):
    
#     text_reply = llm_chat_response(text)
    
#     # Generate audio
#     generator = pipeline(
#         text_reply, 
#         voice=voice,
#         speed=speed,
#         split_pattern=r'\n+'
#     )
    
#     # # Save first segment only for demo
#     # for i, (gs, ps, audio) in enumerate(generator):
#     #     sf.write(f"output_{i}.wav", audio, 24000)
#     #     return FileResponse(
#     #         f"output_{i}.wav",
#     #         media_type="audio/wav",
#     #         filename="output.wav"
#     #     )
    
#     # return Response("No audio generated", status_code=400)


#     # Process only the first segment for demo
#     for i, (gs, ps, audio) in enumerate(generator):

#         # Convert PyTorch tensor to NumPy array
#         audio_numpy = audio.cpu().numpy()
#         # Convert to 16-bit PCM
        
#         # Ensure the audio is in the range [-1, 1]
#         audio_numpy = np.clip(audio_numpy, -1, 1)
#         # Convert to 16-bit signed integers
#         pcm_data = (audio_numpy * 32767).astype(np.int16)
        
#         # Convert to bytes (automatically uses row-major order)
#         raw_audio = pcm_data.tobytes()
        
#         # Return PCM data with minimal necessary headers
#         return Response(
#             content=raw_audio,
#             media_type="application/octet-stream",
#             headers={
#                 "Content-Disposition": f'attachment; filename="output.pcm"',
#                 "X-Sample-Rate": "24000",
#                 "X-Bits-Per-Sample": "16",
#                 "X-Endianness": "little"
#             }
#         )
    
#     return Response("No audio generated", status_code=400)



from fastapi import FastAPI, Response
from fastapi.responses import FileResponse
from kokoro import KPipeline
import os
import numpy as np
import torch 
from huggingface_hub import InferenceClient

def llm_chat_response(text):
    HF_TOKEN = os.getenv("HF_TOKEN")
    client = InferenceClient(
        provider="cerebras",  # Use the provider that supports conversational image-text tasks.
        api_key=HF_TOKEN,
    )
    
    # Build the message payload; here we append a prompt suffix when no image is involved.
    messages = [{
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": text + " describe in one line only"
            }
        ]
    }]
    
    response_from_llama = client.chat.completions.create(
        model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
        messages=messages,
        max_tokens=500,
    )
   
    return response_from_llama.choices[0].message['content']

app = FastAPI()

# Initialize pipeline once at startup
pipeline = KPipeline(lang_code='a')

@app.post("/generate")
async def generate_audio(text: str, voice: str = "af_heart", speed: float = 1.0):
    text_reply = llm_chat_response(text)
    
    # Generate audio using the pipeline
    generator = pipeline(
        text_reply, 
        voice=voice,
        speed=speed,
        split_pattern=r'\n+'
    )
    
    # Process only the first segment for demonstration
    for i, (gs, ps, audio) in enumerate(generator):
        # Convert PyTorch tensor to NumPy array and prepare 16-bit PCM data
        audio_numpy = audio.cpu().numpy()
        audio_numpy = np.clip(audio_numpy, -1, 1)
        pcm_data = (audio_numpy * 32767).astype(np.int16)
        raw_audio = pcm_data.tobytes()
        
        return Response(
            content=raw_audio,
            media_type="application/octet-stream",
            headers={
                "Content-Disposition": 'attachment; filename="output.pcm"',
                "X-Sample-Rate": "24000",
                "X-Bits-Per-Sample": "16",
                "X-Endianness": "little"
            }
        )
    
    return Response("No audio generated", status_code=400)