server files added

.env

@@ -0,0 +1 @@
+GROQ_API_KEY=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

.gitignore

@@ -0,0 +1,6 @@
+
+/server/node_modules
+/server/.venv
+/server/__pycache__
+/server/myenv
+.env.local

Dockerfile

@@ -0,0 +1,29 @@
+# Use an official Python runtime as a parent image
+FROM python:3.11-slim
+
+# Set the working directory in the container
+WORKDIR /code
+
+# Install system dependencies required by your project (ffmpeg, opencv)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy the dependencies file to the working directory
+COPY ./requirements.txt /code/requirements.txt
+
+# Install any needed packages specified in requirements.txt
+# We add gunicorn here for a production-ready web server
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir -r requirements.txt gunicorn
+
+# Copy the rest of the application's code to the working directory
+COPY . /code/
+
+# Expose the port the app runs on (Hugging Face Spaces default is 7860)
+EXPOSE 7860
+
+# Command to run the application using Gunicorn
+CMD ["gunicorn", "--bind", "0.spacing.large0.spacing.large:7860", "--timeout", "600", "app:app"]

app.py

@@ -0,0 +1,281 @@
+from flask import Flask, request, jsonify
+import pymongo
+from routes.auth_routes import auth_bp
+from flask_cors import CORS
+import os
+from werkzeug.utils import secure_filename
+from utils.audioextraction import extract_audio
+from utils.expressions import analyze_video_emotions
+from utils.transcription import speech_to_text_long
+from utils.vocals import predict_emotion
+from utils.vocabulary import evaluate_vocabulary
+from groq import Groq
+from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
+import pandas as pd
+from bson import ObjectId
+import json
+from dotenv import load_dotenv
+from datetime import datetime
+
+load_dotenv()
+app = Flask(__name__)
+CORS(app)
+
+# MongoDB connection
+client = pymongo.MongoClient("mongodb+srv://pmsankheb23:[email protected]/")
+db = client["Eloquence"]
+collections_user = db["user"]
+reports_collection = db["reports"]
+overall_reports_collection = db["overall_reports"]
+
+# Groq client setup
+groq_client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Configure upload folder
+UPLOAD_FOLDER = 'uploads'
+ALLOWED_EXTENSIONS = {'mp4', 'webm', 'wav'}
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
+
+model_id = "firdhokk/speech-emotion-recognition-with-openai-whisper-large-v3"
+model = AutoModelForAudioClassification.from_pretrained(model_id)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True)
+id2label = model.config.id2label
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+def convert_objectid_to_string(data):
+    if isinstance(data, dict):
+        new_dict = {}
+        for k, v in data.items():
+            if isinstance(v, datetime):
+                new_dict[k] = v.isoformat()
+            else:
+                new_dict[k] = convert_objectid_to_string(v)
+        return new_dict
+    elif isinstance(data, list):
+        return [convert_objectid_to_string(item) for item in data]
+    elif isinstance(data, ObjectId):
+        return str(data)
+    return data
+
+app.register_blueprint(auth_bp)
+
+@app.route('/')
+def home():
+    return "Hello World"
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'file' not in request.files:
+        return jsonify({"error": "No file part"}), 400
+    file = request.files['file']
+    context = request.form.get('context', '')
+    title = request.form.get('title', 'Untitled Session')
+    mode = request.form.get('mode', 'video')
+    user_id = request.form.get('userId')
+
+    if not user_id:
+        return jsonify({"error": "User ID is required"}), 400
+
+    if file.filename == '':
+        return jsonify({"error": "No selected file"}), 400
+
+    if file and allowed_file(file.filename):
+        filename = secure_filename(file.filename)
+        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
+        file.save(file_path)
+
+        audio_path = os.path.join(app.config['UPLOAD_FOLDER'], 'output.wav')
+        if not extract_audio(file_path, audio_path):
+            os.remove(file_path)
+            return jsonify({"error": "Failed to process audio from the file"}), 500
+
+        emotion_analysis = pd.DataFrame()
+        if mode == "video":
+            emotion_analysis = analyze_video_emotions(file_path)
+
+        transcription = speech_to_text_long(audio_path)
+        audio_emotion = predict_emotion(audio_path, model, feature_extractor, id2label)
+        vocabulary_report = evaluate_vocabulary(transcription, context)
+        scores = generate_scores(transcription, audio_emotion, emotion_analysis)
+        speech_report = generate_speech_report(transcription, context, audio_emotion)
+        expression_report = generate_expression_report(emotion_analysis) if mode == "video" else "No expression analysis for audio-only mode."
+
+        report_data = {
+            "userId": user_id,
+            "title": title,
+            "context": context,
+            "transcription": transcription,
+            "vocabulary_report": vocabulary_report,
+            "speech_report": speech_report,
+            "expression_report": expression_report,
+            "scores": scores,
+            "createdAt": datetime.utcnow()
+        }
+
+        result = reports_collection.insert_one(report_data)
+        report_data["_id"] = str(result.inserted_id)
+        update_overall_reports(user_id)
+
+        os.remove(file_path)
+        os.remove(audio_path)
+
+        return jsonify(convert_objectid_to_string(report_data)), 200
+    return jsonify({"error": "File type not allowed"}), 400
+
+@app.route('/chat', methods=['POST'])
+def chat():
+    try:
+        data = request.get_json()
+        user_id = data.get('userId')
+        user_message = data.get('message')
+
+        if not user_id or not user_message:
+            return jsonify({"error": "User ID and message are required"}), 400
+
+        user_reports = list(reports_collection.find({"userId": user_id}))
+        reports_summary = "Here is a summary of the user's past performance:\n"
+        for report in user_reports:
+            reports_summary += f"- Session '{report.get('title', 'Untitled')}':\n"
+            reports_summary += f"  - Vocabulary Score: {report['scores']['vocabulary']}\n"
+            reports_summary += f"  - Voice Score: {report['scores']['voice']}\n"
+            reports_summary += f"  - Expressions Score: {report['scores']['expressions']}\n"
+            reports_summary += f"  - Feedback: {report.get('speech_report', '')}\n\n"
+
+        system_message = f"""
+        You are 'Eloquence AI', a friendly and expert public speaking coach. Your goal is to help users improve their speaking skills by providing constructive, encouraging, and actionable feedback.
+
+        User's Past Performance Summary:
+        {reports_summary}
+
+        Based on this history and the user's current message, provide a helpful and encouraging response. Be conversational and supportive.
+        """
+
+        chat_completion = groq_client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": user_message},
+            ],
+            model="llama3-70b-8192",
+        )
+
+        ai_response = chat_completion.choices[0].message.content
+        return jsonify({"response": ai_response}), 200
+
+    except Exception as e:
+        print(f"Error in /chat endpoint: {e}")
+        return jsonify({"error": "An internal error occurred"}), 500
+
+def update_overall_reports(user_id):
+    user_reports = list(reports_collection.find({"userId": user_id}))
+    if not user_reports:
+        return
+
+    num_reports = len(user_reports)
+    avg_vocabulary = sum(r["scores"]["vocabulary"] for r in user_reports) / num_reports
+    avg_voice = sum(r["scores"]["voice"] for r in user_reports) / num_reports
+    avg_expressions = sum(r["scores"]["expressions"] for r in user_reports) / num_reports
+
+    overall_report_data = {
+        "userId": user_id,
+        "avg_vocabulary": avg_vocabulary,
+        "avg_voice": avg_voice,
+        "avg_expressions": avg_expressions,
+        "overall_reports": generate_overall_reports(user_reports)
+    }
+
+    overall_reports_collection.update_one(
+        {"userId": user_id},
+        {"$set": overall_report_data},
+        upsert=True
+    )
+
+@app.route('/user-reports-list', methods=['GET'])
+def get_user_reports_list():
+    user_id = request.args.get('userId')
+    if not user_id:
+        return jsonify({"error": "User ID is required"}), 400
+    user_reports = list(reports_collection.find({"userId": user_id}))
+    if not user_reports:
+        return jsonify([]), 200
+    return jsonify(convert_objectid_to_string(user_reports)), 200
+
+@app.route('/user-reports', methods=['GET'])
+def get_user_reports():
+    user_id = request.args.get('userId')
+    if not user_id:
+        return jsonify({"error": "User ID is required"}), 400
+    overall_report = overall_reports_collection.find_one({"userId": user_id})
+    if not overall_report:
+        return jsonify({"error": "No overall report found for the user"}), 404
+    return jsonify(convert_objectid_to_string(overall_report)), 200
+
+def generate_report(system_message, user_message):
+    try:
+        chat_completion = groq_client.chat.completions.create(
+            messages=[
+                {"role": "system", "content": system_message},
+                {"role": "user", "content": user_message},
+            ],
+            model="llama3-70b-8192",
+        )
+        return chat_completion.choices[0].message.content
+    except Exception as e:
+        print(f"Error generating report: {e}")
+        return "Could not generate report due to an API error."
+
+def generate_overall_reports(user_reports):
+    reports_json = json.dumps(convert_objectid_to_string(user_reports), indent=2)
+    voice_report = generate_report(
+        "You are an expert in speech analysis...",
+        f"Reports: {reports_json}\nProvide a short one paragraph report summarizing the user's overall performance in Voice..."
+    )
+    expressions_report = generate_report(
+        "You are an expert in facial expression analysis...",
+        f"Reports: {reports_json}\nProvide a short one paragraph report summarizing the user's overall performance in Facial Expressions..."
+    )
+    vocabulary_report = generate_report(
+        "You are an expert in language and vocabulary analysis...",
+        f"Reports: {reports_json}\nProvide a short one paragraph report summarizing the user's overall performance in Vocabulary..."
+    )
+    return {
+        "voice_report": voice_report,
+        "expressions_report": expressions_report,
+        "vocabulary_report": vocabulary_report,
+    }
+
+def generate_scores(transcription, audio_emotion, emotion_analysis):
+    system_message = """
+    You are an expert in speech analysis. Based on the provided data, generate scores (out of 100) for Vocabulary, Voice, and Expressions.
+    Provide only the three scores in JSON format, like:
+    {"vocabulary": 85, "voice": 78, "expressions": 90}
+    """
+    emotion_str = emotion_analysis.to_string(index=False) if not emotion_analysis.empty else "No facial data"
+    user_message = f"""
+    Transcription: {transcription}
+    Audio Emotion Data: {audio_emotion}
+    Facial Emotion Analysis: {emotion_str}
+    Provide only the JSON output with numeric scores.
+    """
+    report_content = generate_report(system_message, user_message)
+    try:
+        return json.loads(report_content)
+    except (json.JSONDecodeError, TypeError):
+        return {"vocabulary": 0, "voice": 0, "expressions": 0}
+
+def generate_speech_report(transcription, context, audio_emotion):
+    system_message = f"Context: \"{context}\". Evaluate if emotions in audio match. Emotion data: {audio_emotion}."
+    user_message = "Provide a short one paragraph report on the emotional appropriateness of speech..."
+    return generate_report(system_message, user_message)
+
+def generate_expression_report(emotion_analysis_str):
+    system_message = f"Evaluate the following emotion data: {emotion_analysis_str}."
+    user_message = "Provide a short one paragraph report on the emotional appropriateness of facial expressions..."
+    return generate_report(system_message, user_message)
+
+if __name__ == '__main__':
+    app.run(debug=True)

auth_routes.py

@@ -0,0 +1,94 @@
+from flask import Blueprint, request, jsonify
+from utils.auth import hash_password, check_password, generate_token, verify_token
+import pymongo
+from bson import ObjectId
+
+# Define a Blueprint for authentication routes
+auth_bp = Blueprint('auth', __name__, url_prefix='/auth')
+
+# MongoDB connection
+client = pymongo.MongoClient("mongodb+srv://pmsankheb23:[email protected]/")
+db = client["Eloquence"]
+collections_user = db["user"]
+
+# ROUTE 1: Create a user using POST: auth/create, no auth required
+@auth_bp.route('/create', methods=['POST'])
+def create_user():
+    try:
+        data = request.get_json()
+        username = data['username']
+        email = data['email']
+        password = data['password']
+
+        # Check if user already exists
+        if collections_user.find_one({'email': email}):
+            return jsonify({"error": "User with this email already exists"}), 400
+
+        # Hash the password
+        hashed_password = hash_password(password)
+
+        # Insert the new user
+        result = collections_user.insert_one({'username': username, 'password': hashed_password, 'email': email})
+        user_id = str(result.inserted_id)
+
+        # Generate JWT token
+        token = generate_token(username) # Or email, depending on your token strategy
+
+        return jsonify({
+            "message": "User created",
+            "authToken": token,
+            "userId": user_id,
+            "username": username
+        }), 201
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+# ROUTE 2: Authenticate a user using POST: auth/login, no login required
+@auth_bp.route('/login', methods=['POST'])
+def login_user():
+    try:
+        data = request.get_json()
+        email = data['email']
+        password = data['password']
+
+        user = collections_user.find_one({'email': email})
+        if not user:
+            return jsonify({"error": "User not found"}), 404
+
+        if not check_password(user['password'], password):
+            return jsonify({"error": "Invalid password"}), 401
+
+        user_id = str(user['_id'])
+        username = user['username']
+
+        # Generate JWT token
+        token = generate_token(username) # Or email, consistent with your token strategy
+
+        return jsonify({
+            "message": "Login successful",
+            "token": token,
+            "userId": user_id,
+            "username": username
+        }), 200
+
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+# ROUTE 3: Get logged-in user details using POST: auth/protected, login required
+@auth_bp.route('/protected', methods=['POST'])
+def protected():
+    # Get token from the body as it's a post method
+    token = request.json.get("token", None)
+
+    if not token:
+        return jsonify({"error": "Token missing"}), 401
+
+    # Remove 'Bearer ' from the token string if it's present
+    token = token.replace("Bearer ", "")
+    username = verify_token(token)  # Verify the token
+
+    if not username:
+        return jsonify({"error": "Invalid or expired token"}), 401
+
+    return jsonify({"message": f"Hello, {username}! This is a protected route."})

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@@ -0,0 +1,811 @@
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requirements.txt

@@ -0,0 +1,133 @@
+absl-py
+annotated-types
+anyio
+audeer
+audformat
+audinterface
+audiofile
+audioread
+audmath
+audobject
+audresample
+bcrypt
+blinker
+certifi
+cffi
+charset-normalizer
+click
+colorama
+contourpy
+cryptography
+cycler
+decorator
+distlib
+distro
+dnspython
+exceptiongroup
+facenet-pytorch
+fer
+ffmpeg
+ffmpeg-python
+filelock
+Flask
+Flask-Cors
+fonttools
+fsspec
+future
+groq
+h11
+h5py
+httpcore
+httpx
+huggingface-hub
+idna
+imageio
+imageio-ffmpeg
+importlib_metadata
+iso3166
+iso639-lang
+itsdangerous
+Jinja2
+joblib
+kagglehub
+keras
+kiwisolver
+kociemba
+lazy_loader
+librosa
+llvmlite
+markdown-it-py
+MarkupSafe
+matplotlib
+mdurl
+ml_dtypes
+moviepy
+mpmath
+msgpack
+namex
+networkx
+numba
+numpy
+nvidia-cublas-cu12
+nvidia-cuda-cupti-cu12
+nvidia-cuda-nvrtc-cu12
+nvidia-cuda-runtime-cu12
+nvidia-cudnn-cu12
+nvidia-cufft-cu12
+nvidia-curand-cu12
+nvidia-cusolver-cu12
+nvidia-cusparse-cu12
+nvidia-nccl-cu12
+nvidia-nvjitlink-cu12
+nvidia-nvtx-cu12
+opencv-contrib-python
+opensmile
+optree
+oyaml
+packaging
+pandas
+platformdirs
+pooch
+proglog
+pyarrow
+pyasn1
+pycparser
+pydantic
+pydantic_core
+pydub
+Pygments
+PyJWT
+pymongo
+pyparsing
+python-dateutil
+python-dotenv
+pytz
+PyYAML
+regex
+requests
+rich
+rsa
+safetensors
+scikit-learn
+scipy
+sentencepiece
+six
+sniffio
+sounddevice
+soundfile
+soxr
+SpeechRecognition
+sympy
+threadpoolctl
+tokenizers
+torch
+torchvision
+tqdm
+transformers
+triton
+typing_extensions
+tzdata
+urllib3
+virtualenv
+Werkzeug
+zipp

utils/audioextraction.py

@@ -0,0 +1,30 @@
+import ffmpeg
+import os
+
+def extract_audio(video_file, output_wav):
+    """
+    Extracts audio from a video file and saves it as a WAV file.
+
+    Args:
+        video_file (str): Path to the input video file (e.g., .mp4).
+        output_wav (str): Path to save the extracted audio file.
+
+    Returns:
+        bool: True if extraction is successful, False otherwise.
+    """
+    if not os.path.isfile(video_file):
+        print(f"Error: File '{video_file}' does not exist.")
+        return False
+
+    try:
+        (
+            ffmpeg
+            .input(video_file)
+            .output(output_wav, format='wav', acodec='pcm_s16le')
+            .run(quiet=True, overwrite_output=True)
+        )
+        print(f"Audio successfully extracted to: {output_wav}")
+        return True
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return False

utils/auth.py

@@ -0,0 +1,29 @@
+import jwt
+import bcrypt
+
+SECRET_KEY = "eloquence_key"  
+
+# Hash the password
+def hash_password(password):
+    return bcrypt.hashpw(password.encode('utf-8'), bcrypt.gensalt())
+
+# Check the hashed password
+def check_password(hashed_password, password):
+    return bcrypt.checkpw(password.encode('utf-8'), hashed_password)
+
+# Generate JWT
+def generate_token(username):
+    payload = {
+        'username': username,
+    }
+    return jwt.encode(payload, SECRET_KEY, algorithm='HS256')
+
+# Verify JWT
+def verify_token(token):
+    try:
+        decoded = jwt.decode(token, SECRET_KEY, algorithms=['HS256'])
+        return decoded['username']  # Return username if token is valid
+    except jwt.ExpiredSignatureError:
+        return None  # Token has expired
+    except jwt.InvalidTokenError:
+        return None  # Token is invalid

utils/expressions.py

@@ -0,0 +1,44 @@
+from fer import Video
+from fer import FER
+import pandas as pd
+
+def analyze_video_emotions(video_file_path):
+    """
+    Analyzes the emotions in a given video file and returns a dataframe of scores.
+
+    Args:
+        video_file_path (str): Path to the video file to be analyzed.
+
+    Returns:
+        pd.DataFrame: DataFrame containing the emotion scores.
+    """
+    # Initialize the face detector
+    face_detector = FER(mtcnn=True)
+
+    # Input the video for processing
+    input_video = Video(video_file_path)
+
+    # Analyze the video
+    processing_data = input_video.analyze(face_detector, display=False)
+
+    # Check if any faces were detected
+    if not processing_data:
+        print("No faces detected in the video.")
+        return pd.DataFrame()  # Return an empty DataFrame if no faces are detected
+
+    # Convert the results to a DataFrame
+    vid_df = input_video.to_pandas(processing_data)
+    vid_df = input_video.get_first_face(vid_df)
+    vid_df = input_video.get_emotions(vid_df)
+
+    # Calculate the sum of each emotion
+    emotions = ['angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral']
+    emotions_values = [sum(vid_df[emotion]) for emotion in emotions]
+
+    # Create a DataFrame for comparison
+    score_comparisons = pd.DataFrame({
+        'Human Emotions': [emotion.capitalize() for emotion in emotions],
+        'Emotion Value from the Video': emotions_values
+    })
+
+    return score_comparisons

utils/transcription.py

@@ -0,0 +1,87 @@
+import torch
+from transformers import WhisperProcessor, WhisperForConditionalGeneration
+from pydub import AudioSegment
+import soundfile as sf
+import os
+
+model_name = "openai/whisper-base"
+processor = WhisperProcessor.from_pretrained(model_name)
+model = WhisperForConditionalGeneration.from_pretrained(model_name)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+
+def preprocess_audio(input_audio_path, output_audio_path):
+    """
+    Converts audio to 16kHz WAV format.
+
+    Args:
+        input_audio_path (str): Path to the input audio file.
+        output_audio_path (str): Path to save the processed audio file.
+
+    Returns:
+        str: Path to the processed audio file.
+    """
+    audio = AudioSegment.from_file(input_audio_path)
+    audio = audio.set_frame_rate(16000).set_channels(1)
+    audio.export(output_audio_path, format="wav")
+    return output_audio_path
+
+def split_audio(audio_path, chunk_length_ms=30000):
+    """
+    Splits audio into chunks of specified length.
+
+    Args:
+        audio_path (str): Path to the audio file.
+        chunk_length_ms (int): Length of each chunk in milliseconds.
+
+    Returns:
+        list: List of audio chunks.
+    """
+    audio = AudioSegment.from_file(audio_path)
+    chunks = [audio[i : i + chunk_length_ms] for i in range(0, len(audio), chunk_length_ms)]
+    return chunks
+
+def transcribe_chunk(audio_chunk, chunk_index):
+    """
+    Transcribes a single audio chunk.
+
+    Args:
+        audio_chunk (AudioSegment): The audio chunk to transcribe.
+        chunk_index (int): Index of the chunk.
+
+    Returns:
+        str: Transcription of the chunk.
+    """
+    temp_path = f"temp_chunk_{chunk_index}.wav"
+    audio_chunk.export(temp_path, format="wav")
+    audio, sampling_rate = sf.read(temp_path)
+    inputs = processor(audio, sampling_rate=16000, return_tensors="pt")
+    input_features = inputs.input_features.to(device)
+    predicted_ids = model.generate(input_features)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    os.remove(temp_path)  # Clean up temporary file
+    return transcription
+
+def speech_to_text_long(audio_path):
+    """
+    Transcribes a long audio file by splitting it into chunks.
+
+    Args:
+        audio_path (str): Path to the audio file.
+
+    Returns:
+        str: Full transcription of the audio.
+    """
+    processed_audio_path = "processed_audio.wav"
+    preprocess_audio(audio_path, processed_audio_path)
+
+    # Split audio into chunks
+    chunks = split_audio(processed_audio_path, chunk_length_ms=30000)  # 30 seconds per chunk
+    transcriptions = []
+
+    for idx, chunk in enumerate(chunks):
+        print(f"Transcribing chunk {idx + 1} of {len(chunks)}...")
+        transcription = transcribe_chunk(chunk, idx)
+        transcriptions.append(transcription)
+
+    return " ".join(transcriptions)

utils/vocabulary.py

@@ -0,0 +1,30 @@
+import os
+from groq import Groq
+
+def evaluate_vocabulary(transcription, context):
+    client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+    system_message = f"""
+    Context: {context}
+    Script: {transcription}
+    """
+    user_message = """
+    Evaluate the following speech based on vocabulary. Provide a short report covering:
+    - Vocabulary: Assess the richness, appropriateness, and clarity of the words used.
+    - Highlight if the speech uses engaging and varied language or if it is repetitive or overly simple.
+    - Do not include any scores in the report.
+    """
+    chat_completion = client.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": system_message,
+            },
+            {
+                "role": "user",
+                "content": user_message,
+            }
+        ],
+        model="llama-3.3-70b-versatile",
+    )
+    print(chat_completion.choices[0].message.content)
+    return chat_completion.choices[0].message.content

utils/vocals.py

@@ -0,0 +1,84 @@
+from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
+import librosa
+import torch
+import numpy as np
+
+model_id = "firdhokk/speech-emotion-recognition-with-openai-whisper-large-v3"
+model = AutoModelForAudioClassification.from_pretrained(model_id)
+feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, do_normalize=True)
+id2label = model.config.id2label
+
+def preprocess_audio(audio_array, feature_extractor, sampling_rate, max_length=3000):
+    """
+    Preprocesses audio for emotion prediction.
+
+    Args:
+        audio_array (np.array): The audio data as a numpy array.
+        feature_extractor: The feature extractor for the model.
+        sampling_rate (int): The sampling rate of the audio.
+        max_length (int): Maximum length of the audio features.
+
+    Returns:
+        dict: Preprocessed inputs for the model.
+    """
+    inputs = feature_extractor(
+        audio_array,
+        sampling_rate=sampling_rate,
+        return_tensors="pt",
+    )
+    mel_features = inputs["input_features"]
+    current_length = mel_features.size(2)
+
+    if current_length < max_length:
+        pad_size = max_length - current_length
+        mel_features = torch.nn.functional.pad(mel_features, (0, pad_size), mode="constant", value=0)
+    elif current_length > max_length:
+        mel_features = mel_features[:, :, :max_length]
+
+    inputs["input_features"] = mel_features
+    return inputs
+
+def predict_emotion(audio_path, model, feature_extractor, id2label, sampling_rate=16000, chunk_duration=8.0):
+    """
+    Predicts emotions from an audio file.
+
+    Args:
+        audio_path (str): Path to the audio file.
+        model: The emotion prediction model.
+        feature_extractor: The feature extractor for the model.
+        id2label (dict): Mapping from label IDs to emotion names.
+        sampling_rate (int): The sampling rate of the audio.
+        chunk_duration (float): Duration of each chunk in seconds.
+
+    Returns:
+        list: List of dictionaries containing emotion predictions for each chunk.
+    """
+    audio_array, _ = librosa.load(audio_path, sr=sampling_rate)
+    chunk_length = int(sampling_rate * chunk_duration)
+    num_chunks = len(audio_array) // chunk_length + int(len(audio_array) % chunk_length > 0)
+    
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+
+    results = []
+    for i in range(num_chunks):
+        start = i * chunk_length
+        end = min((i + 1) * chunk_length, len(audio_array))
+        chunk = audio_array[start:end]
+
+        start_time = round(start / sampling_rate, 2)
+        end_time = round(end / sampling_rate, 2)
+
+        inputs = preprocess_audio(chunk, feature_extractor, sampling_rate, max_length=3000)
+        inputs = {key: value.to(device) for key, value in inputs.items()}
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        logits = outputs.logits
+        predicted_id = torch.argmax(logits, dim=-1).item()
+        predicted_label = id2label[predicted_id]
+        
+        results.append({"chunk": i + 1, "start_time": start_time, "end_time": end_time, "emotion": predicted_label})
+
+    return results