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DarkGPTV2/README.md

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+---
+title: DarkGPT
+emoji: 🐨
+colorFrom: green
+colorTo: yellow
+sdk: streamlit
+sdk_version: 1.32.2
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

DarkGPTV2/app.py

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+import streamlit as st
+from g4f.client import Client
+import sqlite3
+import google.generativeai as genai
+# import pyttsx3
+import pyperclip
+import os
+
+api_key = os.getenv("Gemini_api")
+
+
+
+
+st.set_page_config(page_title="DarkGPT",
+                   page_icon="🤖",
+                   initial_sidebar_state="expanded"
+)
+
+
+def local_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+
+
+local_css("style.css")
+
+# Create a connection to the database
+conn = sqlite3.connect('chat_history.db')
+c = conn.cursor()
+
+# Create table if not exists
+try:
+    c.execute('''CREATE TABLE IF NOT EXISTS chat_history
+                 (conversation_id INTEGER, role TEXT, content TEXT)''')
+    conn.commit()
+except Exception as e:
+    st.error(f"An error occurred: {e}")
+
+
+
+# Streamlit app
+def main():
+    try:
+        if "chat_history" not in st.session_state:
+            st.session_state.chat_history = []
+
+        if "conversation_id" not in st.session_state:
+            st.session_state.conversation_id = 1
+
+        models = {
+            "🚀 Airoboros 70B": "airoboros-70b",
+            "👑 Gemini 1.0": "gemini-1.0-pro",
+            "🧨 Gemini 1.0 Pro ": "gemini-1.0-pro-001",
+            "⚡ Gemini 1.0 pro latest": "gemini-1.0-pro-latest",
+            "🔮 Gemini Pro": "gemini-pro",
+        }
+
+        columns = st.columns(3)  # Split the layout into three columns
+        with columns[0]:
+            st.header("DarkGPT")
+
+        with columns[2]:
+
+            selected_model_display_name = st.selectbox("Select Model", list(models.keys()), index=0)
+            selected_model = models[selected_model_display_name]
+
+        with columns[1]:
+            pass
+            # if st.button("summarize"):
+            #     st.switch_page('pages/summarize.py')
+
+
+        # Sidebar (left side) - New chat button
+        if st.sidebar.button("✨ New Chat", key="new_chat_button"):
+            st.session_state.chat_history.clear()
+            st.session_state.conversation_id += 1
+
+        # Sidebar (left side) - Display saved chat
+        st.sidebar.write("Chat History")
+        c.execute("SELECT DISTINCT conversation_id FROM chat_history")
+        conversations = c.fetchall()
+        for conv_id in reversed(conversations):
+            c.execute("SELECT content FROM chat_history WHERE conversation_id=? AND role='bot' LIMIT 1",
+                      (conv_id[0],))
+            first_bot_response = c.fetchone()
+            if first_bot_response:
+                if st.sidebar.button(" ".join(first_bot_response[0].split()[0:5])):
+                    display_conversation(conv_id[0])
+
+        # Sidebar (left side) - Clear Chat History button
+        if st.sidebar.button("Clear Chat History ✖️"):
+            st.session_state.chat_history.clear()
+            c.execute("DELETE FROM chat_history")
+            conn.commit()
+
+        # Main content area (center)
+        st.markdown("---")
+
+        user_input = st.chat_input("Ask Anything ...")
+
+        if user_input:
+            if selected_model == "airoboros-70b":
+                try:
+                    client = Client()
+                    response = client.chat.completions.create(
+                        model=models[selected_model_display_name],
+                        messages=[{"role": "user", "content": user_input}],
+                    )
+                    bot_response = response.choices[0].message.content
+
+                    st.session_state.chat_history.append({"role": "user", "content": user_input})
+                    st.session_state.chat_history.append({"role": "bot", "content": bot_response})
+
+                    # Store chat in the database
+                    for chat in st.session_state.chat_history:
+                        c.execute("INSERT INTO chat_history VALUES (?, ?, ?)",
+                                  (st.session_state.conversation_id, chat["role"], chat["content"]))
+                    conn.commit()
+
+                    # Display chat history
+                    for index, chat in enumerate(st.session_state.chat_history):
+                        with st.chat_message(chat["role"]):
+                            if chat["role"] == "user":
+                                st.markdown(chat["content"])
+                            elif chat["role"] == "bot":
+                                st.markdown(chat["content"])
+
+
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+
+            else:
+                try:    
+                        # GEMINI Replace with your Gemini Api key
+                        GOOGLE_API_KEY = api_key
+                        genai.configure(api_key=GOOGLE_API_KEY)
+                        model = genai.GenerativeModel(selected_model)
+                        prompt = user_input
+                        response = model.generate_content(prompt)
+                        bot_response = response.candidates[0].content.parts[0].text
+
+                        st.session_state.chat_history.append({"role": "user", "content": user_input})
+                        st.session_state.chat_history.append({"role": "bot", "content": bot_response})
+
+                        # Store chat in the database
+                        for chat in st.session_state.chat_history:
+                            c.execute("INSERT INTO chat_history VALUES (?, ?, ?)",
+                                      (st.session_state.conversation_id, chat["role"], chat["content"]))
+                        conn.commit()
+
+                        for index, chat in enumerate(st.session_state.chat_history):
+                            with st.chat_message(chat["role"]):
+                                if chat["role"] == "user":
+                                    st.markdown(chat["content"])
+                                elif chat["role"] == "bot":
+                                    st.markdown(chat["content"])
+
+                except Exception as e:
+                    st.error(f"An error occurred: {e}")
+
+
+
+
+
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+
+
+def display_conversation(conversation_id):
+    c.execute("SELECT * FROM chat_history WHERE conversation_id=?", (conversation_id,))
+    chats = c.fetchall()
+    st.markdown(f"### Conversation")
+    for chat in chats:
+        st.markdown(f"{chat[1]}")
+        st.markdown(f"{chat[2]}")
+
+
+if __name__ == "__main__":
+    main()

DarkGPTV2/cookies.py

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+from g4f.cookies import set_cookies
+
+set_cookies(".bing.com", {
+  "_clck": "w3y6fr%7C2%7Cfjl%7C0%7C1517"
+})
+set_cookies("chat.openai.com", {
+  "__cflb": "0H28vVfF4aAyg2hkHEuhVVUPGkAFmYvk57xoxRMRm1X"
+})
+set_cookies(".google.com", {
+  "__Secure-3PSID": "g.a000hQh_KYcwS81vIoGwzKsyOPzas0X1x5e-k5t1hP-yKs9Glr4Y3QvqZySWierihtij8oAHvgACgYKAcgSAQASFQHGX2Mi2gQjmlI3XdU3m8xFFwtpzRoVAUF8yKoOoZXsIMZcs3KirlD37H8A0076"
+})

DarkGPTV2/gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

DarkGPTV2/howtouse.md

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+# GPT Chat Explorer
+
+GPT Chat Explorer is an interactive web application that allows users to engage in conversations with various GPT (Generative Pre-trained Transformer) models in real-time. This repository contains the source code for the application.
+
+## Features
+
+- **Real-time Conversations:** Engage in natural conversations with GPT models and explore their responses in real-time.
+- **Chat History:** Review and manage chat history organized by conversation ID for easy reference.
+- **Model Selection:** Choose from a selection of GPT models, including GPT-3.5 Turbo, GPT-4, and more.
+- **Custom Styling:** Customize the appearance of the app with custom CSS styles to suit your preferences.
+- **Clear Chat History:** Easily clear chat history with a single click to start fresh.
+- **Interactive Sidebar:** Access key functionalities such as starting a new chat and clearing chat history conveniently from the sidebar.
+- **Error Handling:** Robust error handling ensures smooth operation and provides feedback in case of any issues.
+- **Timestamped Conversations:** View conversation history with timestamps for each message, providing context and chronological order.
+- **Responsive Design:** Enjoy a seamless experience across devices with responsive design elements.
+
+## Getting Started
+
+To get started with the GPT Chat Explorer, follow these steps:
+
+1. **Clone the Repository:**
+   ```
+   git clone https://github.com/codewithdark-git/DarkGPT-Chatbot.git
+   ```
+
+2. **Install Dependencies:**
+   ```
+   pip install -r requirements.txt
+   ```
+
+3. **Run the Application:**
+   ```
+   streamlit run app.py
+   ```
+
+4. **Start Chatting:**
+   Open the application in your web browser and start chatting with GPT models!
+
+## Contributing
+
+Contributions are welcome! If you'd like to contribute to the project, please follow these steps:
+
+1. Fork the repository.
+2. Create a new branch (`git checkout -b feature/new-feature`).
+3. Make your changes and commit them (`git commit -am 'Add new feature'`).
+4. Push to the branch (`git push origin feature/new-feature`).
+5. Create a new Pull Request.
+
+## License
+
+This project is licensed under the MIT License - see the [LICENSE](../Chatbot/LICENSE) file for details.
+
+## Acknowledgements
+
+- This project was inspired by the capabilities of GPT models and the desire to create an interactive chat exploration tool.
+- Special thanks to the Streamlit team for providing a powerful framework for building interactive web applications.

DarkGPTV2/pages/summarize.py

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+import streamlit as st
+import time
+from io import StringIO
+import nltk
+from nltk.tokenize import word_tokenize, sent_tokenize
+from nltk.corpus import stopwords
+import readtime
+import textstat
+from transformers import pipeline
+
+st.set_page_config(page_title="DarkGPT",
+                   page_icon="🤖",
+                   layout="wide",
+                   initial_sidebar_state="expanded"
+)
+
+# Function to summarize text using BART model
+def summarize_text(input_text):
+    summarizer = pipeline("summarization")
+    summarized_text = summarizer(input_text, max_length=10050, min_length=50, do_sample=False)[0]['summary_text']
+    return summarized_text
+
+# Function to analyze text and return metrics
+def analyze_text(input_text):
+    nltk.download('punkt')
+    tokenized_words = word_tokenize(input_text)
+    reading_time = readtime.of_text(input_text)
+    text_complexity = textstat.flesch_reading_ease(input_text)
+    lexical_richness = len(set(tokenized_words)) / len(tokenized_words)
+    num_sentences = len(sent_tokenize(input_text))
+    analysis_results = {
+        'reading_time': reading_time,
+        'text_complexity': text_complexity,
+        'lexical_richness': lexical_richness,
+        'num_sentences': num_sentences
+    }
+    return analysis_results
+
+# Function to display the homepage
+def display_homepage():
+    st.markdown("<h1 style='text-align: center; color: white; font-size:28px;'>Welcome to DarkGPT!🧨</h1>", unsafe_allow_html=True)
+    st.markdown("<h3 style='text-align: center; font-size:56px;'<p>🤖</p></h3>", unsafe_allow_html=True)
+    st.markdown("<h3 style='text-align: center; color: grey; font-size:20px;'>DarkGPT: Your AI text assistant for quick summarization and analysis. Summarize text, analyze complexity, and get insights instantly.!</h3>", unsafe_allow_html=True)
+
+    st.markdown('___')
+    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>What is this App about?<b></h3>", unsafe_allow_html=True)
+    st.write("The provided code is a Streamlit web application named 'DarkGPT' that allows users to summarize and analyze text. Here's an overview of each page and the footer mentioning the author:")
+    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:18px;'><b>Display Functionality:<b></h3>", unsafe_allow_html=True)
+
+    st.markdown("Homepage:\n"
+                "- Displays a welcome message and introduction to the DarkGPT app.\n"
+                "- Provides information about the purpose and target audience of the app.\n"
+                "- Users are encouraged to explore the app's features and contribute to its open-source development on GitHub.")
+
+    st.markdown("Summarization Page:\n"
+                "- Allows users to input text or upload a file for summarization.\n"
+                "- Users can choose between inputting text directly or uploading a file.\n"
+                "- Provides an example text for users to test.\n"
+                "- After input, users can click the 'Summarize' button to generate a summary using the BART model.\n"
+                "- Error messages are displayed if input text length requirements are not met.")
+
+    st.markdown("Analysis Page:\n"
+                "- Similar to the Summarization page, but focuses on analyzing text.\n"
+                "- Users can input text or upload a file for analysis.\n"
+                "- Provides an example text for users to test.\n"
+                "- After input, users can click the 'Analyze' button to generate various metrics such as reading time, text complexity, lexical richness, and number of sentences.\n"
+                "- Error messages are displayed if input text length requirements are not met.")
+
+    footer = '''
+    <footer style="text-align: center; margin-top: 50px; font-family: Arial, sans-serif; color: #555;">
+        <p>Developed by DarkCoder</p>
+        <p>
+            <a href="https://github.com/codewithdark-git" target="_blank" style="text-decoration: none; color: #007bff; margin-right: 10px;">GitHub</a>
+            <a href="https://www.linkedin.com/in/codewithdark/" target="_blank" style="text-decoration: none; color: #007bff; margin-right: 10px;">Linkedin</a>
+            <a href="https://www.facebook.com/codewithdark.fb/" target="_blank" style="text-decoration: none; color: #007bff;">Facebook</a>
+        </p>
+    </footer>
+    '''
+
+    # Display the footer
+    st.markdown(footer, unsafe_allow_html=True)
+
+# Function to display the text summarization page
+def display_summarization_page():
+    st.markdown("<h4 style='text-align: center; color:grey;'>Accelerate knowledge with DarkGPT &#129302;</h4>", unsafe_allow_html=True)
+    st.text('')
+    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:28px;'>Summarize</h3>", unsafe_allow_html=True)
+    st.text('')
+
+    source = st.radio("How would you like to start? Choose an option below", ("I want to input some text", "I want to upload a file"))
+    st.text('')
+
+    s_example = "Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of 'intelligent agents': any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Some popular accounts use the term 'artificial intelligence' to describe machines that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving, however this definition is rejected by major AI researchers. AI applications include advanced web search engines, recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri or Alexa), self-driving cars (such as Tesla), and competing at the highest level in strategic game systems (such as chess and Go). As machines become increasingly capable, tasks considered to require intelligence are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology."
+
+    if source == 'I want to input some text':
+        input_su = st.text_area("Use the example below or input your own text in English (between 1,000 and 10,000 characters)", value=s_example, max_chars=10000, height=330)
+        if st.button('Summarize'):
+            if len(input_su) < 50:
+                st.error('Please enter a text in English of minimum 1,000 characters')
+            else:
+                # Call the summarization function
+                summarized_text = summarize_text(input_su)
+                st.success(summarized_text)
+                st.balloons()
+
+    if source == 'I want to upload a file':
+        file = st.file_uploader('Upload your file here', type=['txt'])
+        if file is not None:
+            stringio = StringIO(file.getvalue().decode("utf-8"))
+            string_data = stringio.read()
+            if len(string_data) < 50 or len(string_data) > 10000:
+                st.error('Please upload a file between 50 and 10,000 characters')
+            else:
+                # Call the summarization function
+                summarized_text = summarize_text(string_data)
+                st.success(summarized_text)
+                st.balloons()
+
+# Function to display the text analysis page
+def display_analysis_page():
+    st.markdown("<h4 style='text-align: center; color:grey;'>Accelerate knowledge with DarkGPT &#129302;</h4>", unsafe_allow_html=True)
+    st.text('')
+    st.markdown("<h3 style='text-align: left; color:#F63366; font-size:28px;'>Analyze</h3>", unsafe_allow_html=True)
+    st.text('')
+
+    a_example = "Artificial intelligence (AI) is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by humans or animals. Leading AI textbooks define the field as the study of 'intelligent agents': any system that perceives its environment and takes actions that maximize its chance of achieving its goals. Some popular accounts use the term 'artificial intelligence' to describe machines that mimic cognitive functions that humans associate with the human mind, such as learning and problem solving, however this definition is rejected by major AI researchers. AI applications include advanced web search engines, recommendation systems (used by YouTube, Amazon and Netflix), understanding human speech (such as Siri or Alexa), self-driving cars (such as Tesla), and competing at the highest level in strategic game systems (such as chess and Go). As machines become increasingly capable, tasks considered to require intelligence are often removed from the definition of AI, a phenomenon known as the AI effect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology."
+
+    source = st.radio("How would you like to start? Choose an option below", ("I want to input some text", "I want to upload a file"))
+    st.text('')
+
+    if source == 'I want to input some text':
+        input_me = st.text_area("Use the example below or input your own text in English (maximum of 10,000 characters)", max_chars=10000, value=a_example, height=330)
+        if st.button('Analyze'):
+            if len(input_me) > 10000:
+                st.error('Please enter a text in English of maximum 10,000 characters')
+            else:
+                # Call the text analysis function
+                analysis_results = analyze_text(input_me)
+                st.write('Reading Time:', analysis_results['reading_time'])
+                st.write('Text Complexity:', analysis_results['text_complexity'])
+                st.write('Lexical Richness:', analysis_results['lexical_richness'])
+                st.write('Number of Sentences:', analysis_results['num_sentences'])
+                st.balloons()
+
+    if source == 'I want to upload a file':
+        file = st.file_uploader('Upload your file here', type=['txt'])
+        if file is not None:
+            stringio = StringIO(file.getvalue().decode("utf-8"))
+            string_data = stringio.read()
+            if len(string_data) > 10000:
+                st.error('Please upload a file of maximum 10,000 characters')
+            else:
+                # Call the text analysis function
+                analysis_results = analyze_text(string_data)
+                st.write('Reading Time:', analysis_results['reading_time'])
+                st.write('Text Complexity:', analysis_results['text_complexity'])
+                st.write('Lexical Richness:', analysis_results['lexical_richness'])
+                st.write('Number of Sentences:', analysis_results['num_sentences'])
+                st.balloons()
+
+# Main function to run the Streamlit app
+def main():
+    st.sidebar.header('DarkGPT, I want to :crystal_ball:')
+    nav = st.sidebar.radio('', ['Go to homepage', 'Summarize text', 'Analyze text'])
+
+    if nav == 'Go to homepage':
+        display_homepage()
+    elif nav == 'Summarize text':
+        display_summarization_page()
+    elif nav == 'Analyze text':
+        display_analysis_page()
+
+if __name__ == "__main__":
+    main()

DarkGPTV2/style.css

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+section[data-testid="stSidebar"] div.stButton button {
+    border: none;
+    color: white;
+    padding: auto; /* Adjust padding */
+    text-align: center;
+    text-decoration: none;
+    display: inline-block;
+    font-size: 14px;
+    margin: -2px -2px;
+    transition-duration: 0.4s;
+    cursor: pointer;
+    border-radius: 10px;
+    box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.5);
+    min-width: 200px; /* Set minimum width */
+    height: auto;
+    white-space: normal; /* Allow long text to wrap */
+    word-wrap: break-word; /* Allow long text to wrap */
+}
+
+section[data-testid="stSidebar"] div.stButton button:hover {
+    background-color: black; /* Darker green on hover */
+}