Update app_utils.py

app_utils.py

@@ -1,9 +1,73 @@
+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
 
-# (Existing imports and functions are assumed to already be here...)
+# === 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)
@@ -27,7 +91,7 @@ def generate_progress_summary(current_feedback, previous_feedback):
             if diff > 0:
                 lines.append(f"✅ **{cat}** improved by **+{diff}**")
             elif diff < 0:
-                lines.append(f"⚠️ **{cat}** dropped by **{diff}**")
+                lines.append(f"⚠️ **{cat}** dropped by **{abs(diff)}**")
             else:
                 lines.append(f"➖ **{cat}** stayed the same")
 
@@ -35,6 +99,7 @@ def generate_progress_summary(current_feedback, previous_feedback):
         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)
@@ -58,7 +123,8 @@ def render_score_chart(data):
     ax.set_title("🎯 Score Comparison")
     return fig
 
-def build_trend_data(sessions, category="Tone"):
+# === Trend Charting (Category Over Time) ===
+def build_trend_data(sessions, category="Clarity"):
     points = []
     timestamps = []
     for s in sessions:
@@ -68,7 +134,7 @@ def build_trend_data(sessions, category="Tone"):
             timestamps.append(s.timestamp.split()[0])
     return timestamps, points
 
-def render_trend_chart(timestamps, points, category="Tone"):
+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")