sss

app.py

@@ -1,214 +1,4 @@
-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()
+x = st.slider('Select a value')
+st.write(x, 'squared is', x * x)