Upload app_utils.py

app_utils.py

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+
+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"
+openai.api_key = os.getenv("OPENAI_API_KEY")
+client = OpenAI(api_key=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