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from fastapi import FastAPI, File, UploadFile, HTTPException
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
import requests
import json
import tempfile
import os
app = FastAPI()
# Set up Whisper model
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3-turbo"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY", "")
OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
@app.post("/transcribe-analyze/")
async def transcribe_analyze(file: UploadFile = File(...)):
try:
# Save the uploaded file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
temp_audio.write(await file.read())
temp_audio_path = temp_audio.name
# Transcribe audio
transcription_result = pipe(temp_audio_path, return_timestamps=True)
transcription = transcription_result["text"]
# Send transcription to AI for classification
response = requests.post(
url=OPENROUTER_URL,
headers={
"Authorization": f"Bearer {OPENROUTER_API_KEY}",
"Content-Type": "application/json"
},
data=json.dumps({
"model": "meta-llama/llama-3.1-70b-instruct:free",
"messages": [
{
"role": "user",
"content": f"You are an AI Assistant that is given the transcript between a call agent and a lead, and you must classify if the lead happily agreed to the booking. The response should have 4 parts: 1. Appointment Booked: Yes/No, 2. Short reason for your answer, 3. Short summary of the call, 4. Lead's overall emotion. \n Here is the transcription: {transcription}",
}
]
})
)
ai_response = response.json().get("choices", [{}])[0].get("message", {}).get("content", "No response from AI.")
# Remove temporary file
os.remove(temp_audio_path)
return {"transcription": transcription, "ai_response": ai_response}
except Exception as e:
return HTTPException(status_code=500, detail=str(e))
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