|
|
| 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 |
|
|
| |
| 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" |
| } |
|
|
| |
| 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 |
|
|
| |
| 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]) |
|
|
| |
| 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 |
|
|
