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import streamlit as st
import requests
# Streamlit interface setup
st.title('Video Summary Interface')
# Creating tabs,
tab1, tab2, tab3, tab4, tab5 = st.tabs(["Project Description", "Video Uploader", "Video Indexer", "Video Prompt", "Unique Value Add"])
with tab1:
st.header("Project Description")
st.write("Here you can describe the project in detail.")
image_path = 'data/projectflow.png'
# Display the image
st.image(image_path, caption='Project Flow Diagram')
# Add more components as needed
with tab2:
st.header("Video Uploader")
st.write("This tab can be used to display Scrum-related data and analytics.")
# You can use things like st.dataframe(df) to show data
image_path2 = 'data/quantumai.png'
# Display the image
st.image(image_path2, caption='Synthetic Data')
# Add more components as needed
# Create a link to an external URL
url = "https://chat.openai.com/g/g-RjiG3D1mm-velocity-scrum-master"
link_text = "Velocity Scrum Master"
# Use Markdown to create the link
st.markdown(f'[**{link_text}**]({url})')
with tab3:
st.header("Video Indexer")
st.write("Information and controls related to the Scrum TruEra Assistants API.")
# Integration and API controls could be managed here
with tab4:
st.header("Video Prompt")
st.write("Information and controls related to the Scrum TruEra Assistants API.")
# 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 tab5:
st.header("Unique Value Add")
st.write("Information and controls related to the Scrum TruEra Assistants API.")
# Integration and API controls could be managed here
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