import os
import requests
from dotenv import load_dotenv
from pydub import AudioSegment
import tempfile
from openai import OpenAI

load_dotenv()
HF_API_URL = os.getenv("HF_API_URL")
HF_API_KEY = os.getenv("HF_API_KEY")
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
openai_client = OpenAI(api_key=OPENAI_API_KEY)

def transcribe_audio(file_path):
    try:
        audio = AudioSegment.from_file(file_path)
        audio = audio.set_frame_rate(16000).set_channels(1)
        temp_wav = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
        audio.export(temp_wav.name, format="wav")
        with open(temp_wav.name, "rb") as f:
            response = openai_client.audio.transcriptions.create(
                file=f,
                model="whisper-1"
            )
        os.remove(temp_wav.name)
        return response.text
    except Exception as e:
        return f"Error in transcription: {e}"

def generate_response(prompt, tone="advisor", model="OpenAI GPT-4o", stream=False):
    try:
        if model == "OpenAI GPT-4o":
            completion = openai_client.chat.completions.create(
                model="gpt-4o",
                messages=[
                    {"role": "system", "content": f"You are an {tone} expert assistant."},
                    {"role": "user", "content": prompt}
                ]
            )
            return completion.choices[0].message.content.strip()

        elif model == "Claude 3 Opus":
            headers = {
                "x-api-key": os.getenv("CLAUDE_API_KEY"),
                "anthropic-version": "2023-06-01"
            }
            data = {
                "model": "claude-3-opus-20240229",
                "messages": [{"role": "user", "content": prompt}]
            }
            response = requests.post(
                "https://api.anthropic.com/v1/messages",
                headers=headers,
                json=data
            )
            return response.json()["content"][0]["text"].strip()

        elif model == "Mistral-7B":
            headers = {"Authorization": f"Bearer {HF_API_KEY}"}
            data = {"inputs": prompt}
            response = requests.post(HF_API_URL, headers=headers, json=data)
            response.raise_for_status()
            return response.json()[0]["generated_text"].strip()

        else:
            return "⚠️ Invalid model selection."

    except Exception as e:
        return f"Error generating response: {e}"