app/main.py

from fastapi import FastAPI, File, UploadFile, HTTPException, Depends, Header
from pydantic import BaseModel
import os
from pymongo import MongoClient
from langchain_community.embeddings import SentenceTransformerEmbeddings
from langchain_community.vectorstores import MongoDBAtlasVectorSearch
import uvicorn
from dotenv import load_dotenv
from fastapi.middleware.cors import CORSMiddleware
from uuid import uuid4

import joblib
import librosa
import numpy as np

import pandas as pd 
import numpy as np
import librosa.display
import soundfile as sf
import opensmile

import ffmpeg
import noisereduce as nr

default_sample_rate=22050

def load(file_name, skip_seconds=0):
    return librosa.load(file_name, sr=None, res_type='kaiser_fast')

# def preprocess_audio(audio_data, rate):
#     # Apply preprocessing steps
#     audio_data = nr.reduce_noise(y=audio_data, sr=rate)
#     audio_data = librosa.util.normalize(audio_data)
#     audio_data, _ = librosa.effects.trim(audio_data)
#     audio_data = librosa.resample(audio_data, orig_sr=rate, target_sr=default_sample_rate)
# #     audio_data = fix_length(audio_data)
#     rate = default_sample_rate

#     return audio_data, rate

def extract_features(X, sample_rate):
    # Generate Mel-frequency cepstral coefficients (MFCCs) from a time series
    mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T,axis=0)

    # Generates a Short-time Fourier transform (STFT) to use in the chroma_stft
    stft = np.abs(librosa.stft(X))

    # Computes a chromagram from a waveform or power spectrogram.
    chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)

    # Computes a mel-scaled spectrogram.
    mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T,axis=0)

    # Computes spectral contrast
    contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)

    # Computes the tonal centroid features (tonnetz)
    tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X),sr=sample_rate).T,axis=0)

    # Concatenate all feature arrays into a single 1D array
    combined_features = np.hstack([mfccs, chroma, mel, contrast, tonnetz])
    return combined_features


load_dotenv()

# MongoDB connection
MONGODB_ATLAS_CLUSTER_URI = os.getenv("MONGODB_ATLAS_CLUSTER_URI", None)
client = MongoClient(MONGODB_ATLAS_CLUSTER_URI)
DB_NAME = "quran_db"
COLLECTION_NAME = "tafsir"
ATLAS_VECTOR_SEARCH_INDEX_NAME = "langchain_index"
MONGODB_COLLECTION = client[DB_NAME][COLLECTION_NAME]


embeddings = SentenceTransformerEmbeddings(model_name="BAAI/bge-m3")

vector_search = MongoDBAtlasVectorSearch.from_connection_string(
    MONGODB_ATLAS_CLUSTER_URI,
    DB_NAME + "." + COLLECTION_NAME,
    embeddings,
    index_name=ATLAS_VECTOR_SEARCH_INDEX_NAME,
)


# FastAPI application setup
app = FastAPI()

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


def index_file(filepath):
    """ Index each block in a file separated by double newlines for quick search. 
    Returns a dictionary with key as content and value as block number. """
    index = {}
    with open(filepath, 'r', encoding='utf-8') as file:
        content = file.read()  # Read the whole file at once
        blocks = content.split("\n\n")  # Split the content by double newlines

        for block_number, block in enumerate(blocks, 1):  # Starting block numbers at 1 for human readability
            # Replace single newlines within blocks with space and strip leading/trailing whitespace
            formatted_block = ' '.join(block.split('\n')).strip()
            index[formatted_block] = block_number
            # if(block_number == 100):
            #     print(formatted_block)  # Print the 5th block

    return index


def get_text_by_block_number(filepath, block_numbers):
    """ Retrieve specific blocks from a file based on block numbers, where each block is separated by '\n\n'. """
    blocks_text = []
    with open(filepath, 'r', encoding='utf-8') as file:
        content = file.read()  # Read the whole file at once
        blocks = content.split("\n\n")  # Split the content by double newlines

        for block_number, block in enumerate(blocks, 1):  # Starting block numbers at 1 for human readability
            if block_number in block_numbers:
                # Replace single newlines within blocks with space and strip leading/trailing whitespace
                formatted_block = ' '.join(block.split('\n')).strip()
                blocks_text.append(formatted_block)
                if len(blocks_text) == len(block_numbers):  # Stop reading once all required blocks are retrieved
                    break
    return blocks_text


# Existing API endpoints
@app.get("/")
async def read_root():
    return {"message": "Welcome to our app"}

# New Query model for the POST request body
class Item(BaseModel):
    question: str

EXPECTED_TOKEN = os.getenv("API_TOKEN")

def verify_token(authorization: str = Header(None)):
    """
    Dependency to verify the Authorization header contains the correct Bearer token.
    """
    # Prefix for bearer token in the Authorization header
    prefix = "Bearer "
    
    # Check if the Authorization header is present and correctly formatted
    if not authorization or not authorization.startswith(prefix):
        raise HTTPException(status_code=401, detail="Unauthorized: Missing or invalid token")

    # Extract the token from the Authorization header
    token = authorization[len(prefix):]

    # Compare the extracted token to the expected token value
    if token != EXPECTED_TOKEN:
        raise HTTPException(status_code=401, detail="Unauthorized: Incorrect token")

# New API endpoint to get an answer using the chain
@app.post("/get_answer")
async def get_answer(item: Item, token: str = Depends(verify_token)):
    try:
        # Perform the similarity search with the provided question
        matching_docs = vector_search.similarity_search(item.question, k=3)
        clean_answers = [doc.page_content.replace("\n", " ").strip() for doc in matching_docs]

        # Assuming 'search_file.txt' is where we want to search answers
        answers_index = index_file('app/quran_tafseer_formatted.txt')

        # Collect line numbers based on answers found
        line_numbers = [answers_index[answer] for answer in clean_answers if answer in answers_index]

        # Assuming 'retrieve_file.txt' is where we retrieve lines based on line numbers
        result_text = get_text_by_block_number('app/quran_tafseer.txt', line_numbers)

        return {"result_text": result_text}
    except Exception as e:
        # If there's an error, return a 500 error with the error's details
        raise HTTPException(status_code=500, detail=str(e))

# random forest
model = joblib.load('app/1713661391.0946255_trained_model.joblib')
pca = joblib.load('app/pca.pkl')
scaler = joblib.load('app/1713661464.8205004_scaler.joblib')
label_encoder = joblib.load('app/1713661470.6730225_label_encoder.joblib')

def preprocess_audio(audio_data, rate):
    audio_data = nr.reduce_noise(y=audio_data, sr=rate)
    # remove silence
    # intervals = librosa.effects.split(audio_data, top_db=20)
    # # Concatenate non-silent intervals
    # audio_data = np.concatenate([audio_data[start:end] for start, end in intervals])

    audio_data = librosa.util.normalize(audio_data)
    audio_data, _ = librosa.effects.trim(audio_data)
    audio_data = librosa.resample(audio_data, orig_sr=rate, target_sr=default_sample_rate)
    rate = default_sample_rate

    # y_trimmed, _ = librosa.effects.trim(y_no_gaps, top_db = 20) 
    # D = librosa.stft(y)
    # S_db = librosa.amplitude_to_db(np.abs(D), ref=np.max)
    # S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128*2,)
    # S_db_mel = librosa.amplitude_to_db(np.abs(S), ref=np.max)
    
    # Apply noise reduction (example using spectral subtraction)
#     y_denoised = librosa.effects.preemphasis(y_trimmed)

#     # Apply dynamic range compression
#     y_compressed = librosa.effects.preemphasis(y_denoised)

#     # Augmentation (example of time stretching)
# #     y_stretched = librosa.effects.time_stretch(y_compressed, rate=1.2)

#     # Silence Removal
#     y_silence_removed, _ = librosa.effects.trim(y_compressed)

#     # Equalization (example: apply high-pass filter)
#     y_equalized = librosa.effects.preemphasis(y_silence_removed)
    
#     # Define target sample rate
#     target_sr = sr

#     # Data Augmentation (example: pitch shifting)
#     y_pitch_shifted = librosa.effects.pitch_shift(y_normalized, sr=target_sr, n_steps=2)


    # Split audio into non-silent intervals

    
    # Normalize the audio signal
    # y_normalized = librosa.util.normalize(y_equalized)

    # Feature Extraction (example: MFCCs)
#     mfccs = librosa.feature.mfcc(y=y_normalized, sr=target_sr, n_mfcc=20)
    
    # output_file_path = os.path.join(save_dir, f"{file_name_without_extension}.{extension}")

    # Write the audio data to the output file in .wav format
    # sf.write(path, y_normalized, target_sr)
    
    return audio_data, rate

# smile = opensmile.Smile(
#     feature_set=opensmile.FeatureSet.ComParE_2016,
#     feature_level=opensmile.FeatureLevel.Functionals,
# )

# def extract_features(file_path):
#     # # Load the audio file
#     # y, sr = librosa.load(file_path, sr=None, dtype=np.float32)
    
#     # # Extract MFCCs
#     # mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
#     # mfccs_mean = pd.Series(mfccs.mean(axis=1), index=[f'mfcc_{i}' for i in range(mfccs.shape[0])])
    
#     # # Extract Spectral Features
#     # spectral_centroids = pd.Series(np.mean(librosa.feature.spectral_centroid(y=y, sr=sr)), index=['spectral_centroid'])
#     # spectral_rolloff = pd.Series(np.mean(librosa.feature.spectral_rolloff(y=y, sr=sr)), index=['spectral_rolloff'])
#     # spectral_flux = pd.Series(np.mean(librosa.onset.onset_strength(y=y, sr=sr)), index=['spectral_flux'])
#     # spectral_contrast = pd.Series(np.mean(librosa.feature.spectral_contrast(S=np.abs(librosa.stft(y)), sr=sr), axis=1), index=[f'spectral_contrast_{i}' for i in range(librosa.feature.spectral_contrast(S=np.abs(librosa.stft(y)), sr=sr).shape[0])])
    
#     # # Extract Pitch
#     # pitches, magnitudes = librosa.piptrack(y=y, sr=sr)
#     # pitch_mean = pd.Series(np.mean(pitches[pitches != 0]), index=['pitch_mean'])  # Average only non-zero values
    
#     # # Extract Zero Crossings
#     # zero_crossings = pd.Series(np.mean(librosa.feature.zero_crossing_rate(y)), index=['zero_crossings'])
    
#     # # Combine all features into a single Series
#     # features = pd.concat([mfccs_mean, spectral_centroids, spectral_rolloff, spectral_flux, spectral_contrast, pitch_mean, zero_crossings])
    
#     features = smile.process_file(file_path)
#     features_reshaped = features.squeeze()

#     # Ensure it's now a 2D structure suitable for DataFrame
#     print("New shape of features:", features_reshaped.shape)

#     all_data = pd.DataFrame([features_reshaped])
#     return all_data

def repair_mp3_with_ffmpeg_python(input_path, output_path):
    """Attempt to repair an MP3 file using FFmpeg."""
    try:
        # Define the audio stream with the necessary conversion parameters
        audio = (
            ffmpeg
            .input(input_path, nostdin=None, y=None)
            .output(output_path, vn=None, acodec='libmp3lame', ar='44100', ac='1', b='192k', af='aresample=44100')
            .global_args('-nostdin', '-y')  # Applying global arguments
            .overwrite_output()
        )
        
        # Execute the FFmpeg command
        ffmpeg.run(audio)
        print(f"File repaired and saved as {output_path}")
    except ffmpeg.Error as e:
        print(f"Failed to repair file {input_path}: {str(e.stderr)}")


@app.post("/mlp")
async def handle_audio(file: UploadFile = File(...)):
    try:
        # Ensure that we are handling an MP3 file
        if file.content_type == "audio/mpeg" or file.content_type == "audio/mp3":
            file_extension = ".mp3"
        elif file.content_type == "audio/wav":
            file_extension = ".wav"
        else:
            raise HTTPException(status_code=400, detail="Invalid file type. Supported types: MP3, WAV.")

        # Read the file's content
        contents = await file.read()
        temp_filename = f"app/{uuid4().hex}{file_extension}"


        # Save file to a temporary file if needed or process directly from memory
        with open(temp_filename, "wb") as f:
            f.write(contents)

        audio_data, sr = load(temp_filename, skip_seconds=5)
        print("finished loading ", temp_filename)
        # Preprocess data
        audio_data, sr = preprocess_audio(audio_data, sr)
        print("finished processing ", temp_filename)
        # Extract features
        features = extract_features(audio_data, sr)

        # preprocess_audio(temp_filename, 'app')
        # repair_mp3_with_ffmpeg_python(temp_filename, temp_filename)
        # # Here you would add the feature extraction logic
        # features = extract_features(temp_filename)
        # print("Extracted Features:", features)
        # features = pca.transform(features)
        # features = np.array(features).reshape(1, -1)
        features = features.reshape(1, -1)

        features = scaler.transform(features)

        # proceed with an inference
        results = model.predict(features)
        # decoded_predictions = [label_encoder.classes_[i] for i in results]

        # # Decode the predictions using the label encoder
        decoded_predictions = label_encoder.inverse_transform(results)
        print('decoded', decoded_predictions[0])
        # .tolist()
        # Clean up the temporary file
        os.remove(temp_filename)
        print({"message": "File processed successfully", "sheikh": decoded_predictions[0]})
        # Return a successful response with decoded predictions
        return {"message": "File processed successfully", "sheikh": decoded_predictions[0]}
    except Exception as e:
        print(e)
        # Handle possible exceptions
        raise HTTPException(status_code=500, detail=str(e))

# if __name__ == "__main__":
#     uvicorn.run("main:app", host="0.0.0.0", port=8080, reload=False)