997d95e over 1 year ago

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# File: handler.py

import base64
import io
import os
import tempfile
from typing import Dict, Any

import cv2
import librosa
import numpy as np
import pandas as pd
import torch
import whisper_timestamped as whisper
from fer import FER
from moviepy.editor import VideoFileClip, AudioFileClip
from torch.nn.functional import softmax
from transformers import AutoModelForAudioClassification, pipeline
from translate import Translator


class EndpointHandler:
    def __init__(self, path=""):
        """
        Loads all models onto the device. This is called once when the endpoint starts.
        """
        print("Loading models...")
        # Use GPU if available
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Using device: {self.device}")

        # 1. Audio Emotion Model
        self.audio_model = AutoModelForAudioClassification.from_pretrained(
            "3loi/SER-Odyssey-Baseline-WavLM-Categorical-Attributes", trust_remote_code=True
        ).to(self.device)
        self.audio_mean = self.audio_model.config.mean
        self.audio_std = self.audio_model.config.std

        # 2. Facial Emotion Model
        self.face_detector = FER(mtcnn=True)

        # 3. Text Emotion Model
        self.text_classifier = pipeline(
            "text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None, device=self.device
        )

        # 4. Transcription Model
        self.transcription_model = whisper.load_model("medium", device=self.device)

        # 5. Translator
        self.translator = Translator(from_lang='ko', to_lang='en')

        print("All models loaded successfully.")

    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Handles an inference request.
        Args:
            data (Dict[str, Any]): Dictionary containing request parameters. Expected keys:
                'video': Base64 encoded video string.
                'analysis_type': One of "audio", "facial", or "text".
        """
        print("Received inference request.")

        # --- 1. Parameter Extraction ---
        if 'inputs' in data and isinstance(data['inputs'], dict):
            params = data['inputs']
        else:
            params = data

        b64_video = params.get("video")
        if not b64_video:
            raise ValueError("Missing 'video' parameter (base64 encoded string)")

        analysis_type = params.get("analysis_type")
        if analysis_type not in ["audio", "facial", "text"]:
            raise ValueError("Missing or invalid 'analysis_type'. Must be 'audio', 'facial', or 'text'.")

        # --- 2. Video Decoding ---
        video_bytes = base64.b64decode(b64_video)

        # Use a temporary file to store the video, as the original functions expect a path
        with tempfile.NamedTemporaryFile(suffix=".mp4", delete=True) as temp_video_file:
            temp_video_file.write(video_bytes)
            temp_video_file.flush()  # Ensure all data is written
            video_path = temp_video_file.name
            print(f"Video saved to temporary file: {video_path}")

            # --- 3. Dispatch to correct analysis function ---
            try:
                if analysis_type == "audio":
                    result = self._analyze_audio_emotions(video_path)
                elif analysis_type == "facial":
                    result = self._detect_faces_and_emotions(video_path)
                elif analysis_type == "text":
                    result = self._process_video_text(video_path)

                print("Analysis completed successfully.")
                return {"status": "success", **result}

            except Exception as e:
                print(f"Error during {analysis_type} analysis: {e}")
                # It's good practice to return a structured error
                return {"status": "error", "message": str(e)}

    # ===================================================================
    #  REFACTORED ANALYSIS FUNCTIONS
    # ===================================================================

    def _analyze_audio_emotions(self, video_path: str) -> Dict:
        temp_audio_path = None
        try:
            # Extract audio
            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio_file:
                temp_audio_path = temp_audio_file.name
                VideoFileClip(video_path).audio.write_audiofile(temp_audio_path, codec="pcm_s16le", logger=None)

            raw_wav, _ = librosa.load(temp_audio_path, sr=self.audio_model.config.sampling_rate)
            norm_wav = (raw_wav - self.audio_mean) / (self.audio_std + 1e-6)

            times, emotions_dfs = [], []
            for start_time in range(0, len(norm_wav), self.audio_model.config.sampling_rate):
                audio_segment = norm_wav[start_time:start_time + self.audio_model.config.sampling_rate]

                # Process segment
                audio_np = np.array(audio_segment)
                mask = torch.ones(1, len(audio_np)).to(self.device)
                wavs = torch.tensor(audio_np).unsqueeze(0).to(self.device)
                with torch.no_grad():
                    pred = self.audio_model(wavs, mask)
                logits = pred.logits if hasattr(pred, 'logits') else pred[0]
                labels = {0: 'Angry', 1: 'Sad', 2: 'Happy', 3: 'Surprise', 4: 'Fear', 5: 'Disgust', 7: 'Neutral'}
                probabilities = softmax(logits, dim=-1).squeeze(0)[[0, 1, 2, 3, 4, 5, 7]]
                probabilities = probabilities / probabilities.sum()
                df = pd.DataFrame([probabilities.cpu().numpy()], columns=labels.values())

                times.append(start_time / self.audio_model.config.sampling_rate)
                emotions_dfs.append(df)

            emotions_df = pd.concat(emotions_dfs, ignore_index=True)
            emotions_df.insert(0, "Time(s)", times)
            emotion_rename_map = {'Angry': 'anger', 'Sad': 'sadness', 'Happy': 'happy', 'Surprise': 'surprise',
                                  'Fear': 'fear', 'Disgust': 'disgust', 'Neutral': 'neutral'}
            emotions_df.rename(columns=emotion_rename_map, inplace=True)

            # Return DataFrame as JSON
            return {"emotions_data": emotions_df.to_json(orient='split')}

        finally:
            if temp_audio_path and os.path.exists(temp_audio_path):
                os.remove(temp_audio_path)

    def _detect_faces_and_emotions(self, video_path: str) -> Dict:
        emotions_data = []
        output_video_path = None

        # ===================================================================
        # NEW: Confidence threshold for filtering false positives.
        # Only faces where at least one emotion has a score > 0.35 will be kept.
        # You can adjust this value. Higher = stricter filtering. (e.g., 0.40)
        # ===================================================================
        CONFIDENCE_THRESHOLD = 0.6

        try:
            # Create a temporary file for the output video
            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_out_video:
                output_video_path = temp_out_video.name

            original_video = VideoFileClip(video_path)
            cap = cv2.VideoCapture(video_path)
            fps = int(cap.get(cv2.CAP_PROP_FPS))
            if fps == 0:  # Handle potential issue with video metadata
                fps = 30
            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

            # Use a temporary path for the video writer intermediate file
            with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_video_writer_file:
                temp_video_writer_path = temp_video_writer_file.name

            out = cv2.VideoWriter(temp_video_writer_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))

            frame_number = 0
            while cap.isOpened():
                ret, frame = cap.read()
                if not ret: break

                time_seconds = round(frame_number / fps)
                result = self.face_detector.detect_emotions(frame)

                # Process each face found in the frame
                for face in result:
                    # ===================================================================
                    # NEW: Filtering logic starts here
                    # ===================================================================
                    emotions = face["emotions"]
                    max_emotion_score = max(emotions.values())

                    # If the highest emotion score is below our threshold, skip this face
                    if max_emotion_score < CONFIDENCE_THRESHOLD:
                        continue  # Ignore this low-confidence detection
                    # ===================================================================
                    # End of new filtering logic. The rest of the loop proceeds as before.
                    # ===================================================================

                    box = face["box"]
                    emotions["Time(s)"] = time_seconds
                    emotions_data.append(emotions)
                    cv2.rectangle(frame, (box[0], box[1]), (box[0] + box[2], box[1] + box[3]), (0, 155, 255), 2)

                    # Find the dominant emotion to display on the video
                    dominant_emotion = max(emotions, key=lambda k: emotions[k] if k != 'Time(s)' else -1)
                    text_to_display = f"{dominant_emotion}: {emotions[dominant_emotion]:.2f}"

                    cv2.putText(frame, text_to_display, (box[0], box[1] - 10),
                                cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv2.LINE_AA)

                out.write(frame)
                frame_number += 1

            cap.release()
            out.release()

            # Combine processed video frames with original audio
            processed_video_clip = VideoFileClip(temp_video_writer_path)
            final_clip = processed_video_clip.set_audio(original_video.audio)
            final_clip.write_videofile(output_video_path, codec='libx264', logger=None)
            os.remove(temp_video_writer_path)  # Clean up intermediate video

            # Read the final video bytes and encode to base64
            with open(output_video_path, "rb") as f:
                processed_video_b64 = base64.b64encode(f.read()).decode("utf-8")

            # Process DataFrame
            emotions_df = pd.DataFrame(emotions_data)
            if not emotions_df.empty:
                emotions_df['Time(s)'] = emotions_df['Time(s)'].round().astype(int)
                max_time = emotions_df['Time(s)'].max()
                all_times = pd.DataFrame({'Time(s)': range(max_time + 1)})
                avg_scores = emotions_df.groupby("Time(s)").mean().reset_index()
                df_merged = pd.merge(all_times, avg_scores, on='Time(s)', how='left').fillna(0)
                df_merged['Time(s)'] = df_merged['Time(s)'].astype(str) + " sec"
            else:
                df_merged = pd.DataFrame()

            return {
                "emotions_data": df_merged.to_json(orient='split'),
                "processed_video": processed_video_b64
            }
        finally:
            if output_video_path and os.path.exists(output_video_path):
                os.remove(output_video_path)

    def _process_video_text(self, video_path: str) -> Dict:
        temp_audio_path = None
        try:
            video_clip = VideoFileClip(video_path)
            with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio_file:
                temp_audio_path = temp_audio_file.name
                video_clip.audio.write_audiofile(temp_audio_path, logger=None)

            audio = whisper.load_audio(temp_audio_path)
            result = whisper.transcribe(self.transcription_model, audio)

            segments_data = [{'text': seg['text'], 'start': seg['start'], 'end': seg['end']} for seg in
                             result['segments']]
            segments_df = pd.DataFrame(segments_data)

            if segments_df.empty:
                return {"words_data": pd.DataFrame().to_json(orient='split'),
                        "segments_data": pd.DataFrame().to_json(orient='split')}

            segments_df['Translated_Text'] = segments_df['text'].apply(lambda x: self.translator.translate(x))
            segments_df['Sentiment_Scores'] = segments_df['Translated_Text'].apply(
                lambda x: {entry['label']: entry['score'] for entry in self.text_classifier(x)[0]})
            sentiment_df = segments_df['Sentiment_Scores'].apply(pd.Series)
            final_segments_df = pd.concat([segments_df.drop(columns=['Sentiment_Scores']), sentiment_df], axis=1)

            # Process words data
            word_texts, word_starts, word_ends = [], [], []
            for segment in result['segments']:
                for word in segment['words']:
                    word_texts.append(word['text'])
                    word_starts.append(word['start'])
                    word_ends.append(word['end'])

            words_df = pd.DataFrame({'text': word_texts, 'start': word_starts, 'end': word_ends})
            words_df['second'] = words_df['start'].apply(lambda x: int(np.ceil(x)))
            words_grouped = words_df.groupby('second').agg(
                {'text': lambda x: ' '.join(x), 'start': 'min', 'end': 'max'}).reset_index()

            max_second = int(video_clip.duration)
            all_seconds = pd.DataFrame({'second': np.arange(0, max_second + 1)})
            words_grouped = all_seconds.merge(words_grouped, on='second', how='left').fillna(
                {'text': '', 'start': 0, 'end': 0})

            emotion_columns = final_segments_df.columns.difference(['text', 'start', 'end', 'Translated_Text'])
            for col in emotion_columns:
                words_grouped[col] = np.nan

            for i, row in words_grouped.iterrows():
                matching_segment = final_segments_df[
                    (final_segments_df['start'] <= row['start']) & (final_segments_df['end'] >= row['end'])]
                if not matching_segment.empty:
                    for emotion in emotion_columns:
                        words_grouped.at[i, emotion] = matching_segment.iloc[0][emotion]

            words_grouped[emotion_columns] = words_grouped[emotion_columns].fillna(0)

            return {
                "words_data": words_grouped.to_json(orient='split'),
                "segments_data": final_segments_df.to_json(orient='split')
            }
        finally:
            if temp_audio_path and os.path.exists(temp_audio_path):
                os.remove(temp_audio_path)

	# File: handler.py

	import base64
	import io
	import os
	import tempfile
	from typing import Dict, Any

	import cv2
	import librosa
	import numpy as np
	import pandas as pd
	import torch
	import whisper_timestamped as whisper
	from fer import FER
	from moviepy.editor import VideoFileClip, AudioFileClip
	from torch.nn.functional import softmax
	from transformers import AutoModelForAudioClassification, pipeline
	from translate import Translator


	class EndpointHandler:
	def __init__(self, path=""):
	"""
	Loads all models onto the device. This is called once when the endpoint starts.
	"""
	print("Loading models...")
	# Use GPU if available
	self.device = "cuda" if torch.cuda.is_available() else "cpu"
	print(f"Using device: {self.device}")

	# 1. Audio Emotion Model
	self.audio_model = AutoModelForAudioClassification.from_pretrained(
	"3loi/SER-Odyssey-Baseline-WavLM-Categorical-Attributes", trust_remote_code=True
	).to(self.device)
	self.audio_mean = self.audio_model.config.mean
	self.audio_std = self.audio_model.config.std

	# 2. Facial Emotion Model
	self.face_detector = FER(mtcnn=True)

	# 3. Text Emotion Model
	self.text_classifier = pipeline(
	"text-classification", model="j-hartmann/emotion-english-distilroberta-base", top_k=None, device=self.device
	)

	# 4. Transcription Model
	self.transcription_model = whisper.load_model("medium", device=self.device)

	# 5. Translator
	self.translator = Translator(from_lang='ko', to_lang='en')

	print("All models loaded successfully.")

	def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
	"""
	Handles an inference request.

	Args:
	data (Dict[str, Any]): Dictionary containing request parameters. Expected keys:
	'video': Base64 encoded video string.
	'analysis_type': One of "audio", "facial", or "text".
	"""
	print("Received inference request.")

	# --- 1. Parameter Extraction ---
	if 'inputs' in data and isinstance(data['inputs'], dict):
	params = data['inputs']
	else:
	params = data

	b64_video = params.get("video")
	if not b64_video:
	raise ValueError("Missing 'video' parameter (base64 encoded string)")

	analysis_type = params.get("analysis_type")
	if analysis_type not in ["audio", "facial", "text"]:
	raise ValueError("Missing or invalid 'analysis_type'. Must be 'audio', 'facial', or 'text'.")

	# --- 2. Video Decoding ---
	video_bytes = base64.b64decode(b64_video)

	# Use a temporary file to store the video, as the original functions expect a path
	with tempfile.NamedTemporaryFile(suffix=".mp4", delete=True) as temp_video_file:
	temp_video_file.write(video_bytes)
	temp_video_file.flush() # Ensure all data is written
	video_path = temp_video_file.name
	print(f"Video saved to temporary file: {video_path}")

	# --- 3. Dispatch to correct analysis function ---
	try:
	if analysis_type == "audio":
	result = self._analyze_audio_emotions(video_path)
	elif analysis_type == "facial":
	result = self._detect_faces_and_emotions(video_path)
	elif analysis_type == "text":
	result = self._process_video_text(video_path)

	print("Analysis completed successfully.")
	return {"status": "success", **result}

	except Exception as e:
	print(f"Error during {analysis_type} analysis: {e}")
	# It's good practice to return a structured error
	return {"status": "error", "message": str(e)}

	# ===================================================================
	# REFACTORED ANALYSIS FUNCTIONS
	# ===================================================================

	def _analyze_audio_emotions(self, video_path: str) -> Dict:
	temp_audio_path = None
	try:
	# Extract audio
	with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio_file:
	temp_audio_path = temp_audio_file.name
	VideoFileClip(video_path).audio.write_audiofile(temp_audio_path, codec="pcm_s16le", logger=None)

	raw_wav, _ = librosa.load(temp_audio_path, sr=self.audio_model.config.sampling_rate)
	norm_wav = (raw_wav - self.audio_mean) / (self.audio_std + 1e-6)

	times, emotions_dfs = [], []
	for start_time in range(0, len(norm_wav), self.audio_model.config.sampling_rate):
	audio_segment = norm_wav[start_time:start_time + self.audio_model.config.sampling_rate]

	# Process segment
	audio_np = np.array(audio_segment)
	mask = torch.ones(1, len(audio_np)).to(self.device)
	wavs = torch.tensor(audio_np).unsqueeze(0).to(self.device)
	with torch.no_grad():
	pred = self.audio_model(wavs, mask)
	logits = pred.logits if hasattr(pred, 'logits') else pred[0]
	labels = {0: 'Angry', 1: 'Sad', 2: 'Happy', 3: 'Surprise', 4: 'Fear', 5: 'Disgust', 7: 'Neutral'}
	probabilities = softmax(logits, dim=-1).squeeze(0)[[0, 1, 2, 3, 4, 5, 7]]
	probabilities = probabilities / probabilities.sum()
	df = pd.DataFrame([probabilities.cpu().numpy()], columns=labels.values())

	times.append(start_time / self.audio_model.config.sampling_rate)
	emotions_dfs.append(df)

	emotions_df = pd.concat(emotions_dfs, ignore_index=True)
	emotions_df.insert(0, "Time(s)", times)
	emotion_rename_map = {'Angry': 'anger', 'Sad': 'sadness', 'Happy': 'happy', 'Surprise': 'surprise',
	'Fear': 'fear', 'Disgust': 'disgust', 'Neutral': 'neutral'}
	emotions_df.rename(columns=emotion_rename_map, inplace=True)

	# Return DataFrame as JSON
	return {"emotions_data": emotions_df.to_json(orient='split')}

	finally:
	if temp_audio_path and os.path.exists(temp_audio_path):
	os.remove(temp_audio_path)

	def _detect_faces_and_emotions(self, video_path: str) -> Dict:
	emotions_data = []
	output_video_path = None

	# ===================================================================
	# NEW: Confidence threshold for filtering false positives.
	# Only faces where at least one emotion has a score > 0.35 will be kept.
	# You can adjust this value. Higher = stricter filtering. (e.g., 0.40)
	# ===================================================================
	CONFIDENCE_THRESHOLD = 0.6

	try:
	# Create a temporary file for the output video
	with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_out_video:
	output_video_path = temp_out_video.name

	original_video = VideoFileClip(video_path)
	cap = cv2.VideoCapture(video_path)
	fps = int(cap.get(cv2.CAP_PROP_FPS))
	if fps == 0: # Handle potential issue with video metadata
	fps = 30
	w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
	h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

	# Use a temporary path for the video writer intermediate file
	with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_video_writer_file:
	temp_video_writer_path = temp_video_writer_file.name

	out = cv2.VideoWriter(temp_video_writer_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))

	frame_number = 0
	while cap.isOpened():
	ret, frame = cap.read()
	if not ret: break

	time_seconds = round(frame_number / fps)
	result = self.face_detector.detect_emotions(frame)

	# Process each face found in the frame
	for face in result:
	# ===================================================================
	# NEW: Filtering logic starts here
	# ===================================================================
	emotions = face["emotions"]
	max_emotion_score = max(emotions.values())

	# If the highest emotion score is below our threshold, skip this face
	if max_emotion_score < CONFIDENCE_THRESHOLD:
	continue # Ignore this low-confidence detection
	# ===================================================================
	# End of new filtering logic. The rest of the loop proceeds as before.
	# ===================================================================

	box = face["box"]
	emotions["Time(s)"] = time_seconds
	emotions_data.append(emotions)
	cv2.rectangle(frame, (box[0], box[1]), (box[0] + box[2], box[1] + box[3]), (0, 155, 255), 2)

	# Find the dominant emotion to display on the video
	dominant_emotion = max(emotions, key=lambda k: emotions[k] if k != 'Time(s)' else -1)
	text_to_display = f"{dominant_emotion}: {emotions[dominant_emotion]:.2f}"

	cv2.putText(frame, text_to_display, (box[0], box[1] - 10),
	cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2, cv2.LINE_AA)

	out.write(frame)
	frame_number += 1

	cap.release()
	out.release()

	# Combine processed video frames with original audio
	processed_video_clip = VideoFileClip(temp_video_writer_path)
	final_clip = processed_video_clip.set_audio(original_video.audio)
	final_clip.write_videofile(output_video_path, codec='libx264', logger=None)
	os.remove(temp_video_writer_path) # Clean up intermediate video

	# Read the final video bytes and encode to base64
	with open(output_video_path, "rb") as f:
	processed_video_b64 = base64.b64encode(f.read()).decode("utf-8")

	# Process DataFrame
	emotions_df = pd.DataFrame(emotions_data)
	if not emotions_df.empty:
	emotions_df['Time(s)'] = emotions_df['Time(s)'].round().astype(int)
	max_time = emotions_df['Time(s)'].max()
	all_times = pd.DataFrame({'Time(s)': range(max_time + 1)})
	avg_scores = emotions_df.groupby("Time(s)").mean().reset_index()
	df_merged = pd.merge(all_times, avg_scores, on='Time(s)', how='left').fillna(0)
	df_merged['Time(s)'] = df_merged['Time(s)'].astype(str) + " sec"
	else:
	df_merged = pd.DataFrame()

	return {
	"emotions_data": df_merged.to_json(orient='split'),
	"processed_video": processed_video_b64
	}
	finally:
	if output_video_path and os.path.exists(output_video_path):
	os.remove(output_video_path)

	def _process_video_text(self, video_path: str) -> Dict:
	temp_audio_path = None
	try:
	video_clip = VideoFileClip(video_path)
	with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio_file:
	temp_audio_path = temp_audio_file.name
	video_clip.audio.write_audiofile(temp_audio_path, logger=None)

	audio = whisper.load_audio(temp_audio_path)
	result = whisper.transcribe(self.transcription_model, audio)

	segments_data = [{'text': seg['text'], 'start': seg['start'], 'end': seg['end']} for seg in
	result['segments']]
	segments_df = pd.DataFrame(segments_data)

	if segments_df.empty:
	return {"words_data": pd.DataFrame().to_json(orient='split'),
	"segments_data": pd.DataFrame().to_json(orient='split')}

	segments_df['Translated_Text'] = segments_df['text'].apply(lambda x: self.translator.translate(x))
	segments_df['Sentiment_Scores'] = segments_df['Translated_Text'].apply(
	lambda x: {entry['label']: entry['score'] for entry in self.text_classifier(x)[0]})
	sentiment_df = segments_df['Sentiment_Scores'].apply(pd.Series)
	final_segments_df = pd.concat([segments_df.drop(columns=['Sentiment_Scores']), sentiment_df], axis=1)

	# Process words data
	word_texts, word_starts, word_ends = [], [], []
	for segment in result['segments']:
	for word in segment['words']:
	word_texts.append(word['text'])
	word_starts.append(word['start'])
	word_ends.append(word['end'])

	words_df = pd.DataFrame({'text': word_texts, 'start': word_starts, 'end': word_ends})
	words_df['second'] = words_df['start'].apply(lambda x: int(np.ceil(x)))
	words_grouped = words_df.groupby('second').agg(
	{'text': lambda x: ' '.join(x), 'start': 'min', 'end': 'max'}).reset_index()

	max_second = int(video_clip.duration)
	all_seconds = pd.DataFrame({'second': np.arange(0, max_second + 1)})
	words_grouped = all_seconds.merge(words_grouped, on='second', how='left').fillna(
	{'text': '', 'start': 0, 'end': 0})

	emotion_columns = final_segments_df.columns.difference(['text', 'start', 'end', 'Translated_Text'])
	for col in emotion_columns:
	words_grouped[col] = np.nan

	for i, row in words_grouped.iterrows():
	matching_segment = final_segments_df[
	(final_segments_df['start'] <= row['start']) & (final_segments_df['end'] >= row['end'])]
	if not matching_segment.empty:
	for emotion in emotion_columns:
	words_grouped.at[i, emotion] = matching_segment.iloc[0][emotion]

	words_grouped[emotion_columns] = words_grouped[emotion_columns].fillna(0)

	return {
	"words_data": words_grouped.to_json(orient='split'),
	"segments_data": final_segments_df.to_json(orient='split')
	}
	finally:
	if temp_audio_path and os.path.exists(temp_audio_path):
	os.remove(temp_audio_path)