app_utils.py

from sqlmodel import SQLModel, Field, create_engine, Session, select
from datetime import datetime
from typing import Optional
import os
import uuid
import subprocess
import re
import json
import matplotlib.pyplot as plt
from faster_whisper import WhisperModel
from openai import OpenAI

# === Setup ===
db_path = "/tmp/chatter_sessions.db"
engine = create_engine(f"sqlite:///{db_path}")
SQLModel.metadata.create_all(engine)

openai_api_key = os.getenv("OPENAI_API_KEY")
client = OpenAI(api_key=openai_api_key)

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

# === Session Table (shared for spoken + written) ===
class SessionEntry(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    user: str
    timestamp: str
    transcript: str
    feedback: str
    language: str

# === Session Utilities ===
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 Audio Utilities (Spoken Only) ===
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])

# === Score Extraction & Comparison Utilities ===
def parse_scores_from_feedback(feedback_text):
    try:
        json_match = re.search(r"\{.*?\}", feedback_text, re.DOTALL)
        if json_match:
            score_block = json.loads(json_match.group(0))
            return {k.strip(): int(v) for k, v in score_block.items() if str(v).isdigit()}
    except Exception as e:
        print("Score parsing failed:", e)
    return {}

def generate_feedback(transcript, language, goal="general improvement", focus_areas=None, previous_transcript=None):
    focus_str = ", ".join(focus_areas) if focus_areas else "Clarity, Structure, Fluency, Content Relevance, and Tone"
    history_section = f"\n\nFor reference, their previous transcript was:\n{previous_transcript}" if previous_transcript else ""

    prompt = f"""
You are a supportive communication coach helping a learner whose goal is: **{goal}**.

Evaluate the user's current speech based on the following areas:
{focus_str}

Give a score out of 10 and a short explanation for each area.

Then provide:
- A summary of strengths and improvement areas.
- One motivational line to end with.

Transcript:
{transcript}
{history_section}
""".strip()

    response = client.chat.completions.create(
        model="gpt-4",
        messages=[
            {"role": "system", "content": f"You are a warm and constructive communication coach responding in {language}."},
            {"role": "user", "content": prompt}
        ],
        temperature=0.7
    )
    return response.choices[0].message.content


def generate_progress_summary(current_feedback, previous_feedback):
    current_scores = parse_scores_from_feedback(current_feedback)
    previous_scores = parse_scores_from_feedback(previous_feedback)
    if not current_scores or not previous_scores:
        return ""

    lines = []
    for cat in current_scores:
        if cat in previous_scores:
            diff = current_scores[cat] - previous_scores[cat]
            if diff > 0:
                lines.append(f"✅ **{cat}** improved by **+{diff}**")
            elif diff < 0:
                lines.append(f"⚠️ **{cat}** dropped by **{abs(diff)}**")
            else:
                lines.append(f"➖ **{cat}** stayed the same")

    if lines:
        return "\n\n**📈 Progress Tracker**\n" + "\n".join(lines)
    return ""

# === Score Charting (Current vs. Previous) ===
def build_score_comparison_data(current_feedback, previous_feedback):
    current = parse_scores_from_feedback(current_feedback)
    previous = parse_scores_from_feedback(previous_feedback)
    categories = list(set(current.keys()).union(set(previous.keys())))
    return {
        "Category": categories,
        "Current Score": [current.get(cat, 0) for cat in categories],
        "Previous Score": [previous.get(cat, 0) for cat in categories]
    }

def render_score_chart(data):
    fig, ax = plt.subplots()
    x = range(len(data["Category"]))
    ax.bar([i - 0.2 for i in x], data["Previous Score"], width=0.4, label='Previous')
    ax.bar([i + 0.2 for i in x], data["Current Score"], width=0.4, label='Current')
    ax.set_xticks(list(x))
    ax.set_xticklabels(data["Category"])
    ax.set_ylim(0, 10)
    ax.legend()
    ax.set_ylabel("Score")
    ax.set_title("🎯 Score Comparison")
    return fig

# === Trend Charting (Category Over Time) ===
def build_trend_data(sessions, category="Clarity"):
    points = []
    timestamps = []
    for s in sessions:
        score = parse_scores_from_feedback(s.feedback).get(category)
        if score is not None:
            points.append(score)
            timestamps.append(s.timestamp.split()[0])
    return timestamps, points

def render_trend_chart(timestamps, points, category="Clarity"):
    fig, ax = plt.subplots()
    ax.plot(timestamps, points, marker="o", linestyle="-", color="blue")
    ax.set_title(f"📈 {category} Progress Over Time")
    ax.set_ylim(0, 10)
    ax.set_ylabel("Score")
    ax.set_xlabel("Date")
    return fig