backup.app.py

import streamlit as st
import json
from bokeh.models.widgets import Div
import base64

# List of URLs 
urls = [
    "https://huggingface.co/spaces/awacke1/CB-GR-Chatbot-Blenderbot",
    "https://huggingface.co/spaces/awacke1/TTS-STT-Blocks",
    "https://huggingface.co/spaces/awacke1/Prompt-Refinery-Text-to-Image-Generation",
    "https://huggingface.co/spaces/awacke1/Video-Summary",
    "https://huggingface.co/spaces/awacke1/AI-MovieMaker-Comedy",
    "https://huggingface.co/spaces/awacke1/ChatGPT-Memory-Chat-Story-Generator",
    "https://huggingface.co/spaces/awacke1/CloneAnyVoice",
    "https://huggingface.co/spaces/awacke1/ChatGPT-Streamlit-2",
    "https://huggingface.co/spaces/awacke1/WikipediaUltimateAISearch",
    "https://huggingface.co/spaces/awacke1/RLHF.Cognitive.Episodic.Semantic.Memory",
    "https://huggingface.co/spaces/awacke1/Memory-Shared",
    "https://huggingface.co/spaces/awacke1/VideoSwap",
    "https://huggingface.co/spaces/awacke1/AI-Wikipedia-Search",
    "https://huggingface.co/spaces/awacke1/AutoMLUsingStreamlit-Plotly",
    "https://huggingface.co/spaces/awacke1/NLP-Lyric-Chorus-Image",
    "https://huggingface.co/spaces/awacke1/OpenAssistant-Chatbot-FTW-Open-Source",
    "https://huggingface.co/spaces/awacke1/ChatGPTStreamlit7",
    "https://huggingface.co/spaces/awacke1/MultiPDF-QA-ChatGPT-Langchain",
    "https://huggingface.co/spaces/awacke1/SOTA-Plan",
    "https://huggingface.co/spaces/awacke1/AIandSmartTools",
    "https://huggingface.co/spaces/awacke1/3DVirtualFood",
    "https://huggingface.co/spaces/awacke1/Gradio-Gallery-Health-Medical-Icon-Sets",
    "https://huggingface.co/spaces/awacke1/DatasetAnalyzer",
    "https://huggingface.co/spaces/awacke1/PrompTart",
    "https://huggingface.co/spaces/awacke1/sileod-deberta-v3-base-tasksource-nli",
    "https://huggingface.co/spaces/awacke1/File-Memory-Operations-Human-Feedback-Gradio",
    "https://huggingface.co/spaces/awacke1/Bloom.Big.Science.Continual.Generator",
    "https://huggingface.co/spaces/awacke1/Ontology-Gradio",
    "https://huggingface.co/spaces/awacke1/HTML5-Aframe-3dMap-Flight",
    "https://huggingface.co/spaces/awacke1/Bloom.Generative.Writer",
    "https://huggingface.co/spaces/awacke1/Voice-ChatGPT-Streamlit-12",
    "https://huggingface.co/spaces/awacke1/HTML5-AR-VR",
    "https://huggingface.co/spaces/awacke1/AnimationAI",
    "https://huggingface.co/spaces/awacke1/GenerativeWordsandImages",
    "https://huggingface.co/spaces/awacke1/AR-VR-IOT-Demo",
    "https://huggingface.co/spaces/awacke1/ArtStyleFoodsandNutrition",
    "https://huggingface.co/spaces/awacke1/CarePlanQnAWithContext",
    "https://huggingface.co/spaces/awacke1/VideoSummaryYoutube3",
    "https://huggingface.co/spaces/awacke1/AW-01ST-CSV-Dataset-Analyzer",
    "https://huggingface.co/spaces/awacke1/Try.Playing.Learning.Sharing.On.This",
    "https://huggingface.co/spaces/awacke1/google-flan-t5-base",
    "https://huggingface.co/spaces/awacke1/PubMed-Parrot-Paraphraser-on-T5",
    "https://huggingface.co/spaces/awacke1/Writing-Grammar-And-Paraphrase-w-Pegasus",
    "https://huggingface.co/spaces/awacke1/runwayml-stable-diffusion-v1-5",
    "https://huggingface.co/spaces/awacke1/DockerGoFlanT5",
    "https://huggingface.co/spaces/awacke1/GradioContinualGenerator",
    "https://huggingface.co/spaces/awacke1/StreamlitSuperPowerCheatSheet"
]

# Extract the last part of each URL (after the last '/') to serve as the name of the button
url_names = [url.split('/')[-1] for url in urls]

# Associate each URL with a relevant emoji based on keywords in its name
emoji_mapping = {
    "Chatbot": "🤖",
    "TTS": "🗣️",
    "STT": "👂",
    "Video": "🎥",
    "MovieMaker": "🍿",
    "ChatGPT": "💬",
    "Voice": "🎙️",
    "Wikipedia": "📖",
    "Memory": "🧠",
    "AI": "🧠",
    "OpenAssistant": "🤝",
    "3D": "🕶️",
    "AR": "👓",
    "VR": "🕶️",
    "Animation": "🖌️",
    "Dataset": "📊",
    "Gradio": "📻",
    "HTML5": "🌐",
    "Writing": "✍️",
    "Grammar": "🖋️",
    "Paraphrase": "🔄",
    "Streamlit": "🌠"
}

# Function to load the history of clicks from the text file
def load_history():
    try:
        with open("click_history.txt", "r") as f:
            return json.load(f)
    except FileNotFoundError:
        return {url: 0 for url in urls}

# Function to save the updated history of clicks to the text file
def save_history(history):
    with open("click_history.txt", "w") as f:
        json.dump(history, f)

# Function to open the URL using the Bokeh model
def navigate_to_link(url):
    js = "window.location.href = '{}'".format(url)  # Current tab
    html = '<img src onerror="{}">'.format(js)
    div = Div(text=html)
    return div

# Function to create a base64 link and return the HTML string
def open_url(url, emoji, name):
    link_name = f"{emoji} {name}"
    b64 = base64.urlsafe_b64encode(url.encode()).decode()  # some strings <-> bytes conversions necessary here
    return f'<a href="data:text/plain;base64,{b64}" download="{link_name}.txt">{link_name}</a>'

# Streamlit app
def streamlit_app():
    # Load the history of clicks
    history = load_history()

    # Display the buttons for each URL
    for url, name, emoji in zip(urls, url_names, emoji_mapping):
        if st.button(f"{emoji} {name}"):
            # Generate the base64 link and display it under the button
            link_html = open_url(url, emoji, name)
            st.markdown(link_html, unsafe_allow_html=True)
            # Open the link using the navigate_to_link function
            div = navigate_to_link(url)
            st.bokeh_chart(div)
            # Update the history of clicks
            history[url] += 1
            save_history(history)
        # Display the number of times the URL was opened below its corresponding button
        st.write(f"Clicked: {history[url]} times")

if __name__ == '__main__':
    streamlit_app()