app.py

import streamlit as st
import requests
from pytube import YouTube
import os
from twelvelabs.models.task import Task

# Streamlit interface setup
st.title('12 Labs - Interview Insight Analyzer')

from twelvelabs import TwelveLabs

client = TwelveLabs(api_key=os.environ.get('TL_API_KEY'))

# Creating tabs, 
tab1, tab2, tab3, tab4 = st.tabs(["Project Description", "Video Uploader", "Video Analyzer", "Unique Value Add"])

with tab1:
    st.header("Project Description")
    st.write("Here you can describe the project in detail.")
    image_path = 'data/data_projectflow.png'

    # Display the image
    st.image(image_path, caption='Project Flow Diagram')
    # Add more components as needed

with tab2:
   
    
    # Function to download YouTube video
    def download_youtube_video(url):
        yt = YouTube(url)
        stream = yt.streams.filter(file_extension='mp4').first()
        video = stream.download()
        return video
    
    st.header('Video Upload and Processing (To Do)')
    
    # Setup your Twelve Labs client
    # Assuming 'client' is set up here (use your actual client initialization)
    # client = TwelveLabsClient(api_key="your_api_key")
    
    # Container for video input
    with st.container():
        st.write("Video Input")
        video_file = st.file_uploader("Upload a video file", type=["mp4", "avi"])
        youtube_url = st.text_input("Or paste a YouTube URL here:")
    
    # Container for video processing output
    with st.container():
        st.write("Video Processing")
    
        if st.button("Process Video"):
            if video_file is not None:
                video_path = video_file.name
                with open(video_path, mode='wb') as f:
                    f.write(video_file.getbuffer())
            elif youtube_url:
                video_path = download_youtube_video(youtube_url)
            else:
                st.warning("Please upload a video file or enter a YouTube URL.")
                st.stop()
    
            print(f"Uploading {video_path}")
            task = client.task.create(index_id="<YOUR_INDEX_ID>", file=video_path, language="en")
            st.success(f"Task id={task.id}")
    
            # Optional: Monitor the video indexing process
            def on_task_update(task: Task):
                st.write(f"Status={task.status}")
    
            task.wait_for_done(callback=on_task_update)
    
            if task.status != "ready":
                st.error(f"Indexing failed with status {task.status}")
            else:
                st.success(f"Uploaded {video_path}. The unique identifier of your video is {task.video_id}.")


with tab3:

    st.header("Video Analyzer")
    st.write("Choose the number of issues you like to examine, and get feedback on how to improve for your next job interview.")
    
    # Creating two columns for layout
    col1, col2 = st.columns(2)
    
    # Embedding YouTube video directly in the left column
    with col1:
        youtube_url = "https://www.youtube.com/watch?v=Uo0KjdDJr1c"
        st.video(youtube_url)
    
    # Using the right column for prompt modification and response
    with col2:
        # Input for modifying the prompt
        prompt = st.text_input("Enter your prompt:", 
                               "list the top 4 job interview mistakes and how to improve")
        
        # Slider to adjust the number in the prompt
        number = st.slider("Select the number of top mistakes:", min_value=1, max_value=10, value=4)
        
        # Update the prompt with the chosen number
        updated_prompt = prompt.replace("4", str(number))
        
        # Button to send the request
        if st.button("Summarize Video"):
            BASE_URL = "https://api.twelvelabs.io/v1.2"
            api_key = "tlk_3CPMVGM0ZPTKNT2TKQ3Y62TA7ZY9"
            data = {
                "video_id": "6636cf7fd1cd5a287c957cf5",
                "type": "summary",
                "prompt": updated_prompt
            }
        
            # Send the request
            response = requests.post(f"{BASE_URL}/summarize", json=data, headers={"x-api-key": api_key})
            
            # Check if the response is successful
            if response.status_code == 200:
                st.text_area("Summary:", response.json()['summary'], height=300)
            else:
                st.error("Failed to fetch summary: " + response.text)
    
    # Run this script using the following command:
    # streamlit run your_script_name.py

with tab4:
    st.header("Top 20 - Unique Value Add")
    import streamlit as st
    
    # List of items
    items = [
        "Standardization and Fairness: Ensuring every candidate is treated equally improves legal compliance and internal fairness.",
        "Improved Hiring Decisions: Objective, data-driven assessments lead to better hires, directly impacting organizational performance.",
        "Time and Cost Efficiency: Reducing the time and resources required for hiring processes translates directly into cost savings.",
        "Scalability: Ability to handle a high volume of interviews efficiently supports rapid scaling, critical for growth phases.",
        "Integration with HR Systems: Streamlining recruitment into broader HR workflows enhances overall HR efficiency.",
        "Predictive Analytics: Advanced analytics can forecast candidate success, improving long-term job fit and satisfaction.",
        "Enhanced Candidate Experience: Providing immediate feedback can enhance reputation and attract quality candidates.",
        "Remote Hiring Efficiency: Facilitates global talent acquisition, crucial for companies with a diverse geographic footprint.",
        "Accessibility and Inclusiveness: Opens up opportunities for a wider pool of candidates, enhancing diversity.",
        "Security and Privacy Compliance: Ensures handling of personal data safely and legally, protecting the company and candidate.",
        "Data-Driven Insights: Offers deep insights into candidate behaviors, refining hiring criteria and outcomes.",
        "Reduced Interviewer Bias: Minimizes human bias, directly contributing to a more diverse and innovative workforce.",
        "Competitive Advantage: Attracting top talent by using cutting-edge technology enhances a company's market positioning.",
        "Reduced Administrative Load: Automates tasks such as scheduling, increasing operational efficiency.",
        "Continuous Improvement Loop: The system's ability to learn and adapt from each interview boosts long-term effectiveness.",
        "Dynamic Questioning: Adapting questions in real-time ensures more relevant and revealing candidate responses.",
        "Documentation and Review: Facilitates compliance and quality control in hiring processes.",
        "AI-Driven Role Matching: Optimizes talent distribution within the organization by matching candidates to suitable roles.",
        "Enhanced Employer Branding: Advances the company's image as innovative and candidate-focused.",
        "Speed of Process: Accelerates the recruitment cycle, reducing downtime and improving responsiveness to staffing needs."
    ]
    
    # Displaying items with numbering in a Streamlit text area
    st.text_area("List of Items", "\n".join([f"{i+1}. {item}" for i, item in enumerate(items)]), height=600)