app.py

from openai import AzureOpenAI
from langchain_openai import AzureChatOpenAI
from huggingface_hub import InferenceClient
import os
import ffmpeg
from typing import List
import nltk
from gtts import gTTS
from sklearn.feature_extraction.text import TfidfVectorizer
from langchain import HuggingFaceHub, PromptTemplate, LLMChain
import gradio as gr
from pytube import YouTube
import requests
import logging
import os
from pydub import AudioSegment
import speech_recognition as sr
import torchaudio
from speechbrain.inference.classifiers import EncoderClassifier
from pydub.silence import split_on_silence
from moviepy.editor import VideoFileClip
nltk.download('punkt')
nltk.download('stopwords')


class VideoAnalytics:
    """
    Class for performing analytics on videos including transcription, summarization, topic generation,
    and extraction of important sentences.
    """

    def __init__(self):
      """
      Initialize the VideoAnalytics object.
      Args:
          hf_token (str): Hugging Face API token.
      """
      # Initialize AzureOpenAI client
      self.client = AzureOpenAI()
      hf_key = os.getenv("HF_TOKEN")
      self.mistral_client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1",token=hf_key)

      # Initialize transcribed text variable
      self.transcribed_text = ""

      self.r = sr.Recognizer()

      self.language_id = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxlingua107-ecapa", savedir="tmp")
      # Initialize english text variable
      self.english_text = ""

      self.openai_llm = AzureChatOpenAI(
          deployment_name="ChatGPT",
      )


      # Configure logging settings
      logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

    def mp3_to_wav(self, mp3_file: str, wav_file: str) -> str:
        """
        Convert an MP3 audio file to WAV format.

        Args:
            mp3_file (str): The path to the input MP3 file.
            wav_file (str): The path to save the output WAV file.

        Returns:
            str: The filename of the converted WAV file.

        Raises:
            Exception: If there's an error during the conversion process.
        """
        try:
            # Load the MP3 file
            audio = AudioSegment.from_mp3(mp3_file)

            # Export the audio to WAV format
            audio.export(wav_file, format="wav")

            logging.info(f"MP3 file '{mp3_file}' converted to WAV successfully: {wav_file}")

            return wav_file
        except Exception as e:
            # Log the exception and raise it further
            logging.error(f"Error occurred while converting MP3 to WAV: {e}")
            raise e

    def split_audio(self, input_file: str) -> List[AudioSegment]:
        """
        Split an audio file into segments of fixed length.

        Args:
            input_file (str): Path to the input audio file.

        Returns:
            List[AudioSegment]: List containing segments of the input audio.
        """
        try:
          # Load the audio file
          audio = AudioSegment.from_file(input_file)

          # Define segment length in milliseconds (5 minutes = 300,000 milliseconds)
          segment_length = 60000

          # Split the audio into segments
          segments = []
          for i, start_time in enumerate(range(0, len(audio), segment_length)):
              # Calculate end time for current segment
              end_time = start_time + segment_length if start_time + segment_length < len(audio) else len(audio)

              # Extract segment
              segment = audio[start_time:end_time]

              # Append segment to list
              segments.append(segment)

          return segments
        except CouldntDecodeError as e:
            logging.error(f"Error decoding audio: {e}")
            return []
    # Function to recognize speech in the audio file
    def transcribe_audio(self,path: str,lang: str):
        """Transcribe speech from an audio file."""
        try:
            with sr.AudioFile(path) as source:
                audio_listened = self.r.record(source)
                text = self.r.recognize_google(audio_listened,language=lang)
            return text
        except sr.UnknownValueError as e:
            logging.error(f"Speech recognition could not understand audio: {e}")
            return ""
        except sr.RequestError as e:
            logging.error(f"Could not request results from Google Speech Recognition service: {e}")
            return ""

    # Function to split the audio file into chunks on silence and apply speech recognition
    def get_large_audio_transcription_on_silence(self,path: str,lang: str):
        """Split the large audio file into chunks and apply speech recognition on each chunk."""
        try:
            sound = AudioSegment.from_file(path)
            chunks = split_on_silence(sound, min_silence_len=500, silence_thresh=sound.dBFS-14, keep_silence=500)
            folder_name = "audio-chunks"

            if not os.path.isdir(folder_name):
                os.mkdir(folder_name)

            whole_text = ""

            for i, audio_chunk in enumerate(chunks, start=1):
                chunk_filename = os.path.join(folder_name, f"chunk{i}.wav")
                audio_chunk.export(chunk_filename, format="wav")

                text = self.transcribe_audio(chunk_filename,lang)

                if text:
                    text = f"{text.capitalize()}. "
                    logging.info(f"Transcribed {chunk_filename}: {text}")
                    whole_text += text
                else:
                    logging.warning(f"No speech recognized in {chunk_filename}")

            return whole_text
        except Exception as e:
            logging.error(f"Error processing audio: {e}")
            return ""

    def transcribe_video(self, vid: str) -> str:
      """
      Transcribe the audio of the video.
      Args:
          vid (str): Path to the video file.
      Returns:
          str: Transcribed text.
      """
      try:
          # Load the video file and extract audio
          video = VideoFileClip(vid)
          audio = video.audio

          # Write audio to a temporary file
          audio.write_audiofile("output_audio.mp3")


          # Replace 'input.mp3' and 'output.wav' with your file paths
          audio_filename = self.mp3_to_wav("output_audio.mp3", 'output.wav')
          segments = self.split_audio(audio_filename)
          splitted_audio_filename = segments[0].export("segment_for_1_min.wav",format="wav")

          # for detect lang
          signal = self.language_id.load_audio(splitted_audio_filename.name)
          prediction =  self.language_id.classify_batch(signal)
          lang = [prediction[3][0].split(":")][0][0]
          text  = self.get_large_audio_transcription_on_silence(audio_filename,lang)
          # Update the transcribed_text attribute with the transcription result
          self.transcribed_text = text
          # Update the translation text into english_text
          self.english_text = self.translation()
          # Return the transcribed text
          return text

      except Exception as e:
          logging.error(f"Error transcribing video: {e}")
          return ""

    def extractive_summary(self,text: str):
        """
        Generate an extractive summary of the input text.

        Args:
            text (str): The input text to be summarized.

        Returns:
            str: The extractive summary of the input text.
        """
        try:
          article_text =text
          # Removing Square Brackets and Extra Spaces
          article_text = re.sub(r'\[[0-9]*\]', ' ', article_text)
          article_text = re.sub(r'\s+', ' ', article_text)
          # Removing special characters and digits
          formatted_article_text = re.sub('[^a-zA-Z]', ' ', article_text )
          formatted_article_text = re.sub(r'\s+', ' ', formatted_article_text)
          sentence_list = nltk.sent_tokenize(article_text)
          stopwords = nltk.corpus.stopwords.words('english')

          word_frequencies = {}
          for word in nltk.word_tokenize(formatted_article_text):
              if word not in stopwords:
                  if word not in word_frequencies.keys():
                      word_frequencies[word] = 1
                  else:
                      word_frequencies[word] += 1
              maximum_frequncy = max(word_frequencies.values())
          for word in word_frequencies.keys():
              word_frequencies[word] = (word_frequencies[word]/maximum_frequncy)
              sentence_scores = {}
          for sent in sentence_list:
              for word in nltk.word_tokenize(sent.lower()):
                  if word in word_frequencies.keys():
                      if len(sent.split(' ')) < 30:
                          if sent not in sentence_scores.keys():
                              sentence_scores[sent] = word_frequencies[word]
                          else:
                              sentence_scores[sent] += word_frequencies[word]
          import heapq
          summary_sentences = heapq.nlargest(12, sentence_scores, key=sentence_scores.get)

          summary = ' '.join(summary_sentences)
          return summary
        except Exception as e:
            logging.error(f"Error occurred during summarization: {e}")
            return ""

    def generate_video_summary(self,model) -> str:
        """
        Generate a summary of the transcribe_video.
        Returns:
            str: Generated summary.
        """
        try:
          if model == "OpenAI":
            # Define a conversation between system and user
            conversation = [
              {"role": "system", "content": "You are a Summarizer"},
              {"role": "user", "content": f"""summarize the following text delimited by triple backticks.Output must in english.give me a detailed summary.abstractive summary working be like summary of what about the given text.don't make bullet points write like a passage.
                        In this format of Outputs given below:
                        Abstractive Summary:
                        ```{self.english_text}```
                """}
                ]
            # Generate completion using ChatGPT model
            response = self.client.chat.completions.create(
                model="GPT-3",
                messages=conversation,
                temperature=0,
                max_tokens=1000
            )
            # Get the generated summary message
            message = response.choices[0].message.content
            return message

          elif model == "Mixtral":
              task = "summary"
              # Generate answer using Mixtral model
              prompt = f"""<s>[INST]summarize the following text delimited by triple backticks.Output must in english.give me a detailed summary.abstractive summary working be like summary of what about the given text.don't make bullet points write like a passage.
                        In this format of Outputs given below:
                        Abstractive Summary:
            ```data:{self.english_text}```[/INST]"""
              result = self.generate(prompt)
              print("self.english_text",self.english_text)
              return result

        except Exception as e:
            logging.error(f"Error generating video summary: {e}")
            return ""


    def generate_topics(self,model) -> str:
        """
        Generate topics from the transcribe_video.
        Returns:
            str: Generated topics.
        """
        try:
          if model == "OpenAI":
            # Define a conversation between system and user
            conversation = [
              {"role": "system", "content": "You are a Topic Generator"},
              {"role": "user", "content": f"""generate single Topics from the following text don't make sentence for topic generation,delimited by triple backticks.Output must in english.
                        list out the topics:
                        Topics:
                        ```{self.english_text}```
                """}
                ]
            # Generate completion using ChatGPT model
            response = self.client.chat.completions.create(
                model="GPT-3",
                messages=conversation,
                temperature=0,
                max_tokens=1000
            )
            # Get the generated topics message
            message = response.choices[0].message.content
            return message
          elif model == "Mixtral":
              task = "topics"
              # Generate answer using Mixtral model
              prompt = f"""<s>[INST]generate single Topics from the following text don't make sentence for topic generation,delimited by triple backticks.Output must in english.
                      list out the topics:
                      Topics:
            ```data:{self.english_text}```[/INST]"""
              result = self.generate(prompt)
              return result

        except Exception as e:
            logging.error(f"Error generating topics: {e}")
            return ""

    def extract_video_important_sentence(self,model) -> str:
        """
        Extract important sentences from the pdf.
        Returns:
            str: Extracted important sentences.
        """
        try:
          if model == "OpenAI":
            # Define a conversation between system and user
            conversation = [
              {"role": "system", "content": "You are a Sentence Extracter"},
              {"role": "user", "content": f""" Extract Most important of the sentences from text.the text is given in triple backtics.
                        listout the sentences:
                        ```{self.english_text}```
                """}
                ]
            # Generate completion using ChatGPT model
            response = self.client.chat.completions.create(
                model="GPT-3",
                messages=conversation,
                temperature=0,
                max_tokens=1000
            )
            # Get the generated topics message
            message = response.choices[0].message.content
            return message
          elif model == "Mixtral":
              task = "topics"
              # Generate answer using Mixtral model
              prompt = f"""<s>[INST] Extract Most important of the sentences from text.the text is given in triple backtics.
                        listout the sentences:
                        ```{self.english_text}```[/INST]"""
              result = self.generate(prompt)
              return result

        except Exception as e:
            logging.error(f"Error Extracting  Important Sentence: {e}")
            return ""



    def translation(self) -> str:
        """
        translation from the transcribed video.
        Returns:
            str: translation.
        """
        try:
          # Define a conversation between system and user
          conversation = [
            {"role": "system", "content": "You are a Multilingual Translator"},
            {"role": "user", "content": f""" Translate the following text in English ,delimited by triple backticks.
                      ```{self.transcribed_text}```
              """}
              ]
          # Generate completion using ChatGPT model
          response = self.client.chat.completions.create(
              model="GPT-3",
              messages=conversation,
              temperature=0,
              max_tokens=1000
          )
          # Get the generated topics message
          message = response.choices[0].message.content
          return message
        except Exception as e:
            logging.error(f"Error generating topics: {e}")
            return ""

    def format_prompt(self, question: str, data: str) -> str:
        """
        Formats the prompt for the language model.
        Args:
            question (str): The user's question.
            data (str): The data to be analyzed.
        Returns:
            str: Formatted prompt.
        """
        prompt = "<s>"
        prompt = f"""[INST] you are the german language and universal language expert .your task is  analyze the given data and user ask any question about given data answer to the user question.your returning answer must in user's language.otherwise reply i don't know.
          data:{data}
          question:{question}[/INST]"""

        prompt1 = f"[INST] {question} [/INST]"
        return prompt+prompt1


    def generate(self, task: str,temperature=0.9, max_new_tokens=5000, top_p=0.95,
                 repetition_penalty=1.0) -> str:
        """
        Generates text based on the prompt and transcribed text.
        Args:
            prompt (str): The prompt for generating text.
            transcribed_text (str): The transcribed text for analysis.
            temperature (float): Controls the randomness of the sampling. Default is 0.9.
            max_new_tokens (int): Maximum number of tokens to generate. Default is 5000.
            top_p (float): Nucleus sampling parameter. Default is 0.95.
            repetition_penalty (float): Penalty for repeating the same token. Default is 1.0.
        Returns:
            str: Generated text.
        """
        try:
          temperature = float(temperature)
          if temperature < 1e-2:
              temperature = 1e-2
          top_p = float(top_p)

          generate_kwargs = dict(
              temperature=temperature,
              max_new_tokens=max_new_tokens,
              top_p=top_p,
              repetition_penalty=repetition_penalty,
              do_sample=True,
              seed=42,
          )

          # Generate text using the mistral client
          stream = self.mistral_client.text_generation(task, **generate_kwargs, stream=True, details=True, return_full_text=False)
          output = ""
          # Concatenate generated text
          for response in stream:
              output += response.token.text
          return output.replace("</s>","")
        except Exception as e:
            logging.error(f"Error in text generation: {e}")
            return "An error occurred during text generation."

    def video_qa(self, question: str, model: str) -> str:
        """
        Performs video question answering.
        Args:
            question (str): The question asked by the user.
            model (str): The language model to be used ("OpenAI" or "Mixtral").
        Returns:
            str: Answer to the user's question.
        """
        try:
          if model == "OpenAI":
            template = """you are the universal language expert .your task is  analyze the given  text and user ask any question about given text answer to the user question.otherwise reply i don't know.
            english_text:{text}
            user_question:{question}"""

            prompt = PromptTemplate(template=template, input_variables=["text","question"])
            llm_chain = LLMChain(prompt=prompt, verbose=True, llm=self.openai_llm)

            # Run the language model chain
            result = llm_chain.run({"text":self.english_text,"question":question})
            return result

          elif model == "Mixtral":
              # Generate answer using Mixtral model
              result = self.generate(question,self.english_text)
              return result
        except Exception as e:
            logging.error(f"Error in video question answering: {e}")
            return "An error occurred during video question answering."


    def write_text_files(self, text: str, filename: str) -> None:
        """
        Write text to a file.
        Args:
            text (str): Text to be written to the file.
            filename (str): Name of the file.
        """
        try:
          file_path = f"{filename}.txt"
          with open(file_path, 'w') as file:
              # Write content to the file
              file.write(text)
        except Exception as e:
            logging.error(f"Error writing text to file: {e}")

    def Download(self, link: str) -> str:
        """
        Download a video from YouTube.
        Args:
            link (str): YouTube video link.
        Returns:
            str: Path to the downloaded video file.
        """
        try:
          # Initialize YouTube object with the provided link
          youtubeObject = YouTube(link)

          # Get the highest resolution stream
          youtubeObject = youtubeObject.streams.get_highest_resolution()
          try:
              # Attempt to download the video
              file_name = youtubeObject.download()
              return file_name
          except:
              # Log any errors that occur during video download
              logging.info("An error has occurred")

          logging.info("Download is completed successfully")

        except Exception as e:
            # Log any errors that occur during initialization of YouTube object
            logging.error(f"Error downloading video: {e}")
            return ""

    def save_audio_with_gtts(self, text: str, filename: str) -> str:
        """
        Generate an audio file from the given text using gTTS and save it.

        Args:
            text (str): The text to be converted into speech.
            filename (str): The filename (including path) to save the audio file.

        Returns:
            str: The filename of the saved audio file.

        Raises:
            Exception: If there's an error during the conversion or saving process.
        """
        try:
            tts = gTTS(text=text, lang='en')
            tts.save(filename)
            logging.info(f"Audio file saved successfully: {filename}")
            return filename
        except Exception as e:
            # Log the exception and raise it further
            logging.error(f"Error occurred while saving audio: {e}")
            raise e

    def main(self, video: str = None, input_path: str = None,model: str = None) -> tuple:
        """
        Perform video analytics.
        Args:
            video (str): Path to the video file.
            input_path (str): Input path for the video.
        Returns:
            tuple: Summary, important sentences, and topics.
        """
        try:
            # Download the video if input_path is provided, otherwise use the provided video path
          if input_path:
            input_path = self.Download(input_path)
            video_ = VideoFileClip(input_path)
            duration = video_.duration
            video_.close()
            if round(duration) <= 6*600:
              text = self.transcribe_video(input_path)
            else:
              return "Video Duration Above 10 Minutes,Try Below 10 Minutes Video","","",None,None,None
          elif video:
            video_ = VideoFileClip(video)
            duration = video_.duration
            video_.close()
            if round(duration) <= 6*600:
              text = self.transcribe_video(video)
              input_path = video
            else:
              return "Video Duration Above 10 Minutes,Try Below 10 Minutes Video","","",None,None,None
          overall_summary = ""
          # Generate summary, important sentences, and topics
          summary = self.generate_video_summary(model)
          extractive_summary = self.extractive_summary(self.english_text)
          overall_summary = summary + "\n\n Extractive Summary: \n\n" + extractive_summary
          self.write_text_files(overall_summary,"Summary")
          summary_voice = self.save_audio_with_gtts(overall_summary,"summary.mp3")
          important_sentences = self.extract_video_important_sentence(model)
          self.write_text_files(important_sentences,"Important_Sentence")
          important_sentences_voice = self.save_audio_with_gtts(important_sentences,"important_sentences.mp3")
          topics = self.generate_topics(model)
          self.write_text_files(topics,"Topics")
          topics_voice = self.save_audio_with_gtts(topics,"topics.mp3")

          # Return the generated summary, important sentences, and topics
          return overall_summary,important_sentences,topics,summary_voice,important_sentences_voice,topics_voice

        except Exception as e:
            # Log any errors that occur during video analytics
            logging.error(f"Error in main function: {e}")
            return "", "", ""

    def gradio_interface(self):
        with gr.Blocks(css="style.css",theme=gr.themes.Soft()) as demo:
            gr.HTML("""<center><h1>Video Analytics</h1></center>""")
            with gr.Row():
              with gr.Column(scale=0.70):
                    yt_link = gr.Textbox(label= "Youtube Link",placeholder="https://www.youtube.com/watch?v=")
              with gr.Column(scale=0.30):
                    model_selection = gr.Dropdown(["OpenAI", "Mixtral"],label="Model",value="model")
            with gr.Row():
              video = gr.Video(sources="upload",height=200,width=300)
            with gr.Row():
              submit_btn = gr.Button(value="Submit")
            with gr.Tab("Summary"):
              with gr.Row():
                  summary = gr.Textbox(show_label=False,lines=10)
              with gr.Row():
                  summary_download = gr.DownloadButton(label="Download",value="Summary.txt",visible=True,size='lg',elem_classes="download_button")
              with gr.Row():
                  summary_audio = gr.Audio(show_label= False,elem_classes='audio_class')
            with gr.Tab("Important Sentences"):
              with gr.Row():
                  Important_Sentences = gr.Textbox(show_label=False,lines=10)
              with gr.Row():
                  sentence_download = gr.DownloadButton(label="Download",value="Important_Sentence.txt",visible=True,size='lg',elem_classes="download_button")
              with gr.Row():
                  important_sentence_audio = gr.Audio(show_label = False,elem_classes='audio_class')
            with gr.Tab("Topics"):
              with gr.Row():
                  Topics = gr.Textbox(show_label=False,lines=10)
              with gr.Row():
                  topics_download = gr.DownloadButton(label="Download",value="Topics.txt",visible=True,size='lg',elem_classes="download_button")
              with gr.Row():
                  topics_audio = gr.Audio(show_label=False,elem_classes='audio_class')
            with gr.Tab("Video QA"):
              with gr.Row():
                with gr.Column(scale=0.70):
                  question = gr.Textbox(show_label=False,placeholder="Ask Your Questions...")
                with gr.Column(scale=0.30):
                  model = gr.Dropdown(["OpenAI", "Mixtral"],show_label=False,value="model")
              with gr.Row():
                  result = gr.Textbox(label='Answer',lines=10)
              submit_btn.click(self.main,[video,yt_link,model_selection],[summary,Important_Sentences,Topics,summary_audio,important_sentence_audio,topics_audio])
              question.submit(self.video_qa,[question,model],result)
        demo.launch()

if __name__ == "__main__":
  video_analytics = VideoAnalytics()
  video_analytics.gradio_interface()