import os
import uuid
import subprocess
from datetime import datetime
from sqlmodel import SQLModel, Field, create_engine, Session, select
from typing import Optional
from faster_whisper import WhisperModel
from openai import OpenAI

# === Setup ===
db_path = "/tmp/chatter_sessions.db"
client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))

LANG_CODES = {
    "English": "en", "Spanish": "es", "Hindi": "hi", "French": "fr", "German": "de",
    "Arabic": "ar", "Chinese": "zh", "Portuguese": "pt", "Japanese": "ja", "Korean": "ko"
}

# === SQLModel setup ===
class SessionEntry(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    user: str
    timestamp: str
    transcript: str
    feedback: str
    language: str

engine = create_engine(f"sqlite:///{db_path}")
SQLModel.metadata.create_all(engine)

def save_to_db(user, transcript, feedback, language):
    session = Session(engine)
    entry = SessionEntry(
        user=user,
        timestamp=datetime.now().strftime("%Y-%m-%d %H:%M"),
        transcript=transcript,
        feedback=feedback,
        language=language
    )
    session.add(entry)
    session.commit()
    session.close()

def fetch_user_sessions(user):
    session = Session(engine)
    statement = select(SessionEntry).where(SessionEntry.user == user)
    results = session.exec(statement).all()
    session.close()
    return results

# === Whisper ===
model = WhisperModel("base", compute_type="int8")

def convert_to_wav(input_file):
    output_wav = f"/tmp/{uuid.uuid4()}.wav"
    command = ["ffmpeg", "-y", "-i", input_file, "-ar", "16000", "-ac", "1", output_wav]
    subprocess.run(command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
    return output_wav

def transcribe_audio(audio_path):
    segments, _ = model.transcribe(audio_path)
    return " ".join([segment.text for segment in segments])

# === GPT ===
def generate_feedback(transcript, language):
    prompt = f"""You are a communication coach. Please respond in [language={language}].
Evaluate the user's speech on:
1. Clarity
2. Structure
3. Fluency
4. Content Relevance
5. Tone & Expression
Each category:
- Score out of 10
- Short explanation
End with:
- Overall feedback summary
- One motivational line
Transcript:
{transcript}
"""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"You are a supportive communication coach responding in {language}."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7
    )
    return response.choices[0].message.content

def generate_example_response(transcript, language):
    prompt = f"""You are a communication coach. Rewrite this speech to make it more polished, fluent, and confident.
Keep the meaning and tone the same, but improve clarity and structure.
Transcript:
{transcript}
"""
    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"Reply in {language}. Provide only the improved version of the speech."},
            {"role": "user", "content": prompt}
        ]
    )
    return response.choices[0].message.content