from fastapi import FastAPI, File, UploadFile
from pydantic import BaseModel
import os
import torchaudio
import torch.nn.functional as F
import torch
from transformers import AutoProcessor, AutoModelForAudioClassification, pipeline
from pathlib import Path

app_dir = Path(__file__).parent

# Deepfake model setup
deepfake_model_path = app_dir / "Deepfake" / "model"
deepfake_processor = AutoProcessor.from_pretrained(deepfake_model_path)
deepfake_model = AutoModelForAudioClassification.from_pretrained(
    pretrained_model_name_or_path=deepfake_model_path,
    local_files_only=True,
)

def prepare_audio(file_path, sampling_rate=16000, duration=10):
    waveform, original_sampling_rate = torchaudio.load(file_path)
    if waveform.shape[0] > 1:
        waveform = torch.mean(waveform, dim=0, keepdim=True)
    if original_sampling_rate != sampling_rate:
        resampler = torchaudio.transforms.Resample(orig_freq=original_sampling_rate, new_freq=sampling_rate)
        waveform = resampler(waveform)
    chunk_size = sampling_rate * duration
    audio_chunks = []
    for start in range(0, waveform.shape[1], chunk_size):
        chunk = waveform[:, start:start + chunk_size]
        if chunk.shape[1] < chunk_size:
            padding = chunk_size - chunk.shape[1]
            chunk = torch.nn.functional.pad(chunk, (0, padding))
        audio_chunks.append(chunk.squeeze().numpy())
    return audio_chunks

def predict_audio(file_path):
    audio_chunks = prepare_audio(file_path)
    predictions = []
    confidences = []
    for chunk in audio_chunks:
        inputs = deepfake_processor(
            chunk, sampling_rate=16000, return_tensors="pt", padding=True
        )
        with torch.no_grad():
            outputs = deepfake_model(**inputs)
            logits = outputs.logits
            probabilities = F.softmax(logits, dim=1)
            confidence, predicted_class = torch.max(probabilities, dim=1)
            predictions.append(predicted_class.item())
            confidences.append(confidence.item())
    aggregated_prediction_id = max(set(predictions), key=predictions.count)
    predicted_label = deepfake_model.config.id2label[aggregated_prediction_id]
    average_confidence = sum(confidences) / len(confidences)
    return {
        "predicted_label": predicted_label,
        "average_confidence": average_confidence
    }

# ScamText model setup
scamtext_pipe = pipeline("text-classification", model="phishbot/ScamLLM")

# Input model for scam text inference
class TextInput(BaseModel):
    input: str


# Initialize FastAPI
app = FastAPI()

@app.post("/deepfake/infer")
async def deepfake_infer(file: UploadFile = File(...)):
    temp_file_path = f"temp_{file.filename}"
    with open(temp_file_path, "wb") as temp_file:
        temp_file.write(await file.read())
    try:
        predictions = predict_audio(temp_file_path)
    finally:
        os.remove(temp_file_path)
    return predictions

@app.post("/scamtext/infer")
async def scamtext_infer(data: TextInput):
    predictions = scamtext_pipe(data.input)
    return predictions

@app.get("/deepfake/health")
async def deepfake_health():
    return {
        "message": "ok",
        "Sound": str(torchaudio.list_audio_backends())
    }

@app.get("/scamtext/health")
async def scamtext_health():
    return {"message": "ok"}

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)