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app.py

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+import subprocess
+# # Run the pip install command
+subprocess.check_call(['pip', 'install', 'wordcloud'])
+subprocess.check_call(['pip', 'install', 'git+https://github.com/openai/whisper.git'])
+subprocess.check_call(['pip', 'install', 'transformers'])
+subprocess.check_call(['pip', 'install', 'imageio==2.4.1'])
+subprocess.check_call(['pip', 'install', 'moviepy'])
+subprocess.check_call(['pip', 'install', 'keybert'])
+
+subprocess.check_call(['pip', 'install', 'pytube'])
+
+import streamlit as st
+import os
+from wordcloud import WordCloud
+from keybert import KeyBERT
+import pandas as pd
+import matplotlib.pyplot as plt
+# //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+
+
+from moviepy.editor import *
+from tqdm import tqdm
+import os
+import math
+import nltk
+nltk.download('punkt')
+import whisper
+from transformers import pipeline
+
+from pytube import YouTube
+def process_video(path):
+    whisper_model = whisper.load_model("base")
+    
+    def SpeechToTextEng(aud_path):
+      result = whisper_model.transcribe(aud_path)
+      return result["text"]
+        
+    def run_range(duration):
+        time=duration/60
+        floor=math.ceil(time)
+        return floor
+
+    time_range=60
+    clip_run_range=0
+    clip_duration=0
+
+    def audio_generator(path,aud=0,vid=0):
+        if vid==1:
+            clip=VideoFileClip(path)
+            clip_duration = clip.duration
+            clip_run_range=run_range(clip_duration)
+            for i in range(clip_run_range):
+                left=i*time_range
+                right=left+time_range
+                # print(left,right)
+
+                crop_clip=clip.subclip(left,right)
+                try:
+                    crop_clip.audio.write_audiofile("vid_to_aud"+str(i)+".mp3")
+                except:
+                    pass
+
+        if aud==1:
+            audio_clip=AudioFileClip(path)
+            clip_duration = audio_clip.duration
+            print(clip_duration)
+            clip_run_range=run_range(clip_duration)
+            print(clip_run_range)
+            for i in range(clip_run_range):
+                left=i*time_range
+                right=left+time_range
+                # print(left,right)
+                crop_clip=audio_clip.subclip(left,right)
+                try:
+                    crop_clip.write_audiofile("vid_to_aud"+str(i)+".mp3")
+                except:
+                    pass
+            
+    
+    
+
+    # YouTube video URL
+    video_url = path
+    
+    # Create a YouTube object
+    yt = YouTube(video_url)
+    
+    # Get the highest resolution video stream
+    stream = yt.streams.get_lowest_resolution()
+    
+    # Download the video
+    stream.download(filename='meeting.mp4')
+    
+    audio_generator("./meeting.mp4",vid=1)
+    transcribed_lit=[]
+    label_lit=[]
+    translated_lit=[]
+
+    for i in tqdm(range(clip_run_range)):
+        transcribed=SpeechToTextEng("./vid_to_aud"+str(i)+".mp3")
+        transcribed_lit.append(transcribed)
+        os.remove("./vid_to_aud"+str(i)+".mp3")
+
+
+    data = pd.DataFrame(
+        {'transcriptions': transcribed_lit
+        })
+
+    summarizer = pipeline("summarization")
+
+    sentiment_analyzer = pipeline("sentiment-analysis")
+
+    sumarized_lit=[]
+    sentiment_lit=[]
+    for i in tqdm(range(len(data))):
+        summarized=summarizer(data.iloc[i,0],min_length=75, max_length=300)[0]['summary_text']
+        sentiment = sentiment_analyzer(data.iloc[i,0])[0]['label']
+        sumarized_lit.append(summarized)
+        sentiment_lit.append(sentiment)
+
+    data['summary']=sumarized_lit
+    data['sentiment']=sentiment_lit
+    data.to_csv('output2.csv', index=False)
+    tot_text=""
+    for i in range(len(data)):
+        tot_text=tot_text+data.iloc[i,0]
+
+    key_model = KeyBERT('distilbert-base-nli-mean-tokens')
+    def extract_keywords(text, top_n=50):
+        keywords = key_model.extract_keywords(text, top_n=top_n)
+        return [keyword[0] for keyword in keywords]
+
+    tot_keywords=extract_keywords(tot_text)
+
+    def get_500_words(text,left,right):
+        words = text.split()
+        first_500_words = ' '.join(words[left:right])
+        return first_500_words
+
+    def summarize_text(text):
+        chunk_size = 500  # Number of words per chunk
+        total_summary = ""  # Total summary
+
+        words = text.split()  # Split the text into individual words
+        num_chunks = len(words) // chunk_size + 1  # Calculate the number of chunks
+
+        for i in tqdm(range(num_chunks)):
+            start_index = i * chunk_size
+            end_index = start_index + chunk_size
+            chunk = " ".join(words[start_index:end_index])
+
+            # Pass the chunk to the summarizer (replace with your summarization code)
+            chunk_summary = summarizer(chunk,min_length=75, max_length=200)[0]['summary_text']
+            # print(chunk_summary)
+            total_summary += chunk_summary
+
+        return total_summary
+
+    tot_summary=summarize_text(tot_text)
+    return tot_text,tot_summary,tot_keywords
+
+
+
+
+# //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
+def generate_word_cloud(text):
+    # Create a WordCloud object
+    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)
+
+    # Display the generated word cloud
+    fig, ax = plt.subplots(figsize=(10, 5))
+
+    # Plot the word cloud on the axis
+    ax.imshow(wordcloud, interpolation='bilinear')
+    ax.axis('off')
+    st.pyplot(fig)
+
+
+def main():
+    st.title("Meeting Summary Web App")
+
+    # YouTube link input
+    youtube_url = st.text_input("Enter the YouTube video link")
+
+    if st.button("Process Video"):
+        if youtube_url:
+            # Process the YouTube video
+            tot_text, tot_summary, tot_keywords = process_video(youtube_url)
+
+            # Display the output
+            if os.path.exists("output2.csv"):
+                output_df = pd.read_csv("output2.csv")
+                st.subheader("Transcriptions:")
+                st.write(output_df["transcriptions"])
+
+                st.subheader("Labels:")
+                st.write(output_df["labels"])
+
+                st.subheader("Word Cloud:")
+                generate_word_cloud(output_df["transcriptions"].str.cat(sep=' '))
+
+                st.subheader("tot_text:")
+                st.write(tot_text)
+
+                st.subheader("tot_summary:")
+                st.write(tot_summary)
+
+                st.subheader("tot_keywords:")
+                st.write(tot_keywords)
+
+            else:
+                st.write("No output file found.")
+
+if __name__ == "__main__":
+    main()