app.py

from fastapi import FastAPI, File, UploadFile, HTTPException, Form, Request
from fastapi.responses import JSONResponse
import torch
import os
from pydub import AudioSegment
import tempfile
from transformers import pipeline as transorm_pipline,AutoModelForSpeechSeq2Seq, AutoProcessor
from openai import OpenAI
from pydantic import BaseModel
import logging
import subprocess
from dotenv import load_dotenv
import re
import requests
from typing import Optional, Dict
import time
import numpy as np
import io

app = FastAPI()
load_dotenv()  
UPLOAD_FOLDER = tempfile.gettempdir()
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
os.environ['TRANSFORMERS_CACHE'] = '/asif/cache/'

logging.basicConfig(level=logging.INFO)

fluency_pipe = transorm_pipline("audio-classification", model="megathil/fluency_prediction")

# Define device and torch_dtype
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32

# Define the Hugging Face model and processor
distil_model_id = "distil-whisper/distil-small.en"


distil_model = AutoModelForSpeechSeq2Seq.from_pretrained(
    distil_model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
distil_model.to(device)

processor = AutoProcessor.from_pretrained(distil_model_id)

async def hello_world():
    return  {
        "Hello World"
    }

client = OpenAI(
    api_key=os.environ['AUTH_TOKEN_AI'],
    base_url="https://api.deepinfra.com/v1/openai",
)


class EnhanceTextRequest(BaseModel):
    text: str

class AnalyzeTextRequest(BaseModel):
    text: str
    question: str

class AnalyzeTextResponse(BaseModel):
    relevance_rating: int
    vocabulary_rating: int

class User(BaseModel):
    firstName: str
    lastName: Optional[str]
    city: Optional[str] = None
    college: Optional[str] = None
    department: Optional[str] = None
    course: Optional[str] = None

class ModuleModel(BaseModel):
    questions: Optional[Dict[str, str]] = None
    unitId: Optional[str] = None
    moduleId: Optional[str] = None

class MyModel(BaseModel):
    userProgressStatusId: str

# Define the enhance_text function
def enhance_text(text, question):
    response = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        messages=[
            {"role": "system", "content": "You will be provided with a job interview question and answer.Your task is to rewrite the answer in standard English, making it more comprehensive and suitable for an fresher in interview. Ensure the response is at least 80 words and includes any relevant standard points related to the question.Be concise and omit disclaimers."},
            {"role": "user", "content": f"question:{question} \n Answer:{text}"}
        ],
        temperature=0.7,
        max_tokens=400,
        top_p=1
    )
    enhanced_text = response.choices[0].message.content
    return enhanced_text

def extract_rating_and_reason(text, rating_pattern, reason_pattern):
    rating_match = re.search(rating_pattern, text, re.IGNORECASE)
    reason_match = re.search(reason_pattern, text, re.IGNORECASE | re.DOTALL)
    
    if rating_match and reason_match:
        rating = int(rating_match.group(1))
        reason = reason_match.group(1).strip()
        return {"rating": rating, "reason": reason}
    else:
        return {"rating": "", "reason": "Unable to retrieve rating and reason due to insufficient information."}

def vocabulary_text(text, question):
    response = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        messages=[
            {"role": "system", "content": "You will be provided with question and answer, and your task is to check the rate the Vocabulary in the answer in the range of 1-100 and reasoning (Provide answer in this format vocablary_rating : rating, reason: , in 100 words and Respond without any introductory or conclusion text)"},
            {"role": "user", "content": f"question:{question} \n Answer:{text}"}
        ],
        temperature=0.7,
        max_tokens=250,
        top_p=1
    )
    
    vocabulary_rating = response.choices[0].message.content
    print("vocabulary_rating-->",vocabulary_rating)
    # Define patterns to match rating and reason
    vocab_rating_pattern = r"(?:vocabulary\s*rating|vocab\.\s*rating|vocab\s*rating|rating)\s*:\s*(\d+)"
    reason_pattern = r"reason\s*:\s*(.*)"

    # Extract and format the rating and reason
    result = extract_rating_and_reason(vocabulary_rating, vocab_rating_pattern, reason_pattern)
    formatted_vocabulary_rating = {"Vocabulary_rating": result["rating"], "reason": result["reason"]}
    
    return formatted_vocabulary_rating

def relevance_check(text, question):
    response = client.chat.completions.create(
        model="meta-llama/Meta-Llama-3-8B-Instruct",
        messages=[
            {"role": "system", "content": "You will be provided with question and answer from an job interview and your task is to check the Answer is relevant to the question in the range of 1-100 and reasoning (Provide answer in this format relevance_rating : rating , reason: , in 100 words and Respond without any introductory or conclusion text)"},
            {"role": "user", "content": f"question:{question} \n Answer:{text}"}
        ],
        temperature=0.7,
        max_tokens=250,
        top_p=1
    )
    relevance_rating = response.choices[0].message.content
    print("relevance_rating-->",relevance_rating)
    # Define patterns to match rating and reason
    rel_rating_pattern = r"(?:relevance\s*rating|rel\.\s*rating|rel\s*rating|rating)\s*:\s*(\d+)"
    reason_pattern = r"reason\s*:\s*(.*)"

    # Extract and format the rating and reason
    result = extract_rating_and_reason(relevance_rating, rel_rating_pattern, reason_pattern)
    formatted_relevance_rating = {"Relevance_rating": result["rating"], "reason": result["reason"]}
    
    return formatted_relevance_rating

def transcribe_audio(file_path: str) -> str:
    whisper = transorm_pipline(
        "automatic-speech-recognition",
        model=distil_model,
        tokenizer=processor.tokenizer,
        feature_extractor=processor.feature_extractor,
        max_new_tokens=128,
        torch_dtype=torch_dtype,
        device=device,
    )
    transcription = whisper(file_path, chunk_length_s=30, stride_length_s=5, batch_size=8)
    return transcription

def calculate_speech_rate(text, audio_duration):
    # Calculate the word count from the text
    full_transcription = text['text']
    word_count = len(full_transcription.split())
    logging.info(f"Total word count: {word_count}")
    # Calculate the speech rate in words per minute
    speech_rate_wpm = word_count / audio_duration * 60
    
    # Define minimum and maximum speech rates for normalization
    min_speech_rate = 80  # words per minute (0%)
    max_speech_rate = 200  # words per minute (100%)
    
    # Normalize the speech rate to a percentage
    if speech_rate_wpm < min_speech_rate:
        speech_rate_percentage = 0
    elif speech_rate_wpm > max_speech_rate:
        speech_rate_percentage = 100
    else:
        speech_rate_percentage = ((speech_rate_wpm - min_speech_rate) / 
                                  (max_speech_rate - min_speech_rate)) * 100
    
    return speech_rate_percentage

@app.post("/process_audio_master")
async def process_audio(
    request: Request,
    audio_file: UploadFile = File(...),
    question: Optional[str] = Form("Tell me about yourself?"),
    unitId: Optional[str] = Form(""),
    moduleId: Optional[str] = Form(""),
    questionId: Optional[str] = Form(""),
    userProgressStatusId: Optional[str] = Form(""),
    firstName: Optional[str] = Form(""),
    lastName: Optional[str] = Form(""),
    city: Optional[str] = Form(""),
    college: Optional[str] = Form(""),
    department: Optional[str] = Form(""),
    course: Optional[str] = Form(""),
    userId: Optional[str] = Form("")

):
    # Logging the raw request data
    logging.info(f"Request headers: {request.headers}")
    logging.info(f"Request query params: {request.query_params}")
    logging.info(f"Request path params: {request.path_params}")
    logging.info(f"Request cookies: {request.cookies}")

    # Logging form data and file details
    logging.info(f"Received audio file: {audio_file.filename}")
    logging.info(f"Received question: {question}")
    user = {
        "firstName": firstName,
        "lastName": lastName,
        "city": city,
        "college": college,
        "department": department,
        "course": course,
    }
    logging.info(user)
    start_time = time.time()
    try:
        file_path = os.path.join(UPLOAD_FOLDER, audio_file.filename)
        with open(file_path, "wb") as f:
            f.write(await audio_file.read())
            
        filename = audio_file.filename
        file_size_bytes = os.path.getsize(file_path)
        file_size_mb = file_size_bytes / (1024 * 1024)
        logging.info(f"File Size: {file_size_mb:.2f} MB")
        if filename.endswith(('.mp4', '.avi', '.mkv', '.mov')):
            try:
                output_audio_path = os.path.join(UPLOAD_FOLDER, 'output_audio.mp3')
                command = ['ffmpeg', '-i', file_path, '-vn', '-acodec', 'libmp3lame', '-y', output_audio_path]
                subprocess.run(command, check=True)
                audio_file_path = output_audio_path
            except Exception as e:
                return JSONResponse(content={'error': f'Error converting video to audio: {str(e)}'}, status_code=500)
        elif filename.endswith('.mp3'):
            try:
                output_audio_path = os.path.join(UPLOAD_FOLDER, filename.rsplit('.', 1)[0] + '.wav')
                command = ['ffmpeg', '-i', file_path, '-acodec', 'pcm_s16le', '-ar', '44100', '-ac', '2', '-y', output_audio_path]
                subprocess.run(command, check=True)
                audio_file_path = output_audio_path
            except Exception as e:
                return JSONResponse(content={'error': f'Error converting MP3 to WAV: {str(e)}'}, status_code=500)
        else:
            audio_file_path = file_path

        audio = AudioSegment.from_file(audio_file_path)
        audio_duration = audio.duration_seconds
        logging.info(f"Audio Duration: {audio_duration}")
        
        if audio_duration < 15:
            data = {
            "code":400,
            "messageCode":"1020",
            "message": "Recording Duration too short."
            }
            
            return JSONResponse(content=data)
        
        transcribe_results = transcribe_audio(audio_file_path)
        
        if not transcribe_results['text'].strip():
            data = {
            "code":400,
            "messageCode":"1020",
            "message": "No content in Audio"
        }
            
            
        elif len(transcribe_results['text'].split()) < 20:
            data = {
            "code":400,
            "messageCode":"1020",
            "message": "Audio lacks content"
            }
      
            
            return JSONResponse(content=data)
        else:
            logging.info(f"transcribed content from the audio: {transcribe_results}")
        
        transcribed_content = transcribe_results['text']
        # logging.info(f"transcribed content from the audio: {transcribed_content}")
        logging.info(f"Audio Duration: {audio_duration}")
        # Trim audio to 10 seconds for emotion and fluency analysis
        trimmed_audio = audio[:10000]  # First 10 seconds
        trimmed_audio_path = os.path.join(UPLOAD_FOLDER, 'trimmed_audio.wav')
        trimmed_audio.export(trimmed_audio_path, format="wav")
        
        # Get the volume level of the audio
        volume_db = get_audio_volume_level(audio)
        volume_classification, score = classify_volume_level(volume_db)
        emotionRecognition={
        "label": volume_classification, "score": score
        }
        fluency_results = fluency_pipe(trimmed_audio_path)
        max_result = max(fluency_results, key=lambda x: x['score'])
        fluencyLevel = {
            "label": max_result['label'],
            "score": round(max_result['score'], 2)
        }
        # fluencyLevel = {
        #     "label": "Fluent",
        #    "score":  0.99
        # }

        enhance_text_result = enhance_text(transcribe_results['text'], question)

        relevance_rating = relevance_check(transcribe_results['text'], question)
        contentRelevanceRating = relevance_rating['Relevance_rating']
        contentRelevanceRatingReason = relevance_rating['reason']
    
        voacablary_level = vocabulary_text(transcribe_results['text'], question)
        vocabularyRating = voacablary_level['Vocabulary_rating']
        vocabularyRatingReason = voacablary_level['reason']

        speech_rate = calculate_speech_rate(transcribe_results, audio_duration)

        os.remove(file_path)
        os.remove(trimmed_audio_path)
        if audio_file_path != file_path:
            os.remove(audio_file_path)
        
        node_data = {
            "aiToken": "25b0b507a8c467b4bd0d011df44fe72b6a4aa38ee60a868f3705745c45254aa6",
            "userId": userId,
            "userProgressStatusId": userProgressStatusId,
            "unitId": unitId,
            "moduleId": moduleId,
            "questionId": questionId,
             "aiResponseData": {
            'contentRelevanceRating':contentRelevanceRating,
            'contentRelevanceRatingReason':contentRelevanceRatingReason,
            'vocabularyRating':vocabularyRating,
            'vocabularyRatingReason':vocabularyRatingReason,
            "transcription": transcribe_results,
            "emotionRecognition": emotionRecognition,
            "fluencyLevel": fluencyLevel,
            "enhancedText": enhance_text_result,
            "speechRate": speech_rate,
            }
        }
        data = {
            "code": 200,
            "messageCode":"1004",
            "data":{
            'contentRelevanceRating':contentRelevanceRating,
            'contentRelevanceRatingReason':contentRelevanceRatingReason,
            'vocabularyRating':vocabularyRating,
            'vocabularyRatingReason':vocabularyRatingReason,
            "transcription": transcribe_results,
            "emotionRecognition": emotionRecognition,
            "fluencyLevel": fluencyLevel,
            "enhancedText": enhance_text_result,
            "speechRate": speech_rate,
            },
            "message": "Record(s) Found."
        }
        node_server_url = "https://megathil.shenll.com/processAudio/saveUserResult"
        requests.post(node_server_url, json=node_data)
        end_time = time.time()  # Record end time
        elapsed_time = end_time - start_time  # Calculate elapsed time
        logging.info(f"Total processing time: {elapsed_time:.2f} seconds")
        
        return JSONResponse(content=data)

    except Exception as e:
        logging.error(f"Error processing request: {e}")
        return JSONResponse(content={"error": str(e)}, status_code=500)
    
def get_audio_volume_level(audio: AudioSegment) -> float:
    # Convert the audio data to a numpy array
    samples = np.array(audio.get_array_of_samples(), dtype=np.float32)
    
    # Calculate the root mean square (RMS) of the samples
    rms = np.sqrt(np.mean(samples**2))
    
    # Convert RMS to decibels (dB)
    if rms == 0:
        rms_db = -np.inf  # Use -infinity dB for silence
    else:
        rms_db = 20 * np.log10(rms)
    
    return rms_db

def classify_volume_level(volume_db: float) -> str:
    """Classify the audio volume level into 'High', 'Medium', or 'Low' with specific scores."""
    if volume_db == -np.inf or volume_db < 50.0:
        return "Low", 40.00
    elif 50.0 <= volume_db < 70.0:
        return "Medium", 60.00
    elif volume_db >= 70.0:
        return "High", 100.00
    else:
        return "High", 100.00