from huggingface_hub import InferenceClient
import gradio as gr

client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1")

def format_prompt(message, history):
  prompt = "<s>"
  for user_prompt, bot_response in history:
    prompt += f"[INST] {user_prompt} [/INST]"
    prompt += f" {bot_response}</s> "
  prompt += f"[INST] {message} [/INST]"
  return prompt

def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    formatted_prompt = format_prompt(prompt, history)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        output += response.token.text
        yield output
    return output

additional_inputs=[
    gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs",),
    gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=1048, step=64, interactive=True, info="The maximum numbers of new tokens",),
    gr.Slider(label="Top-p (nucleus sampling)",value=0.90,minimum=0.0,maximum=1,step=0.05,interactive=True,info="Higher values sample more low-probability tokens",),
    gr.Slider(label="Repetition penalty",value=1.2,minimum=1.0,maximum=2.0,step=0.05,interactive=True,info="Penalize repeated tokens",)
]

css = """#mkd {height: 200px; overflow: auto; border: 1px solid #ccc;}"""
with gr.Blocks(css=css) as demo:
    gr.ChatInterface(
        generate,
        additional_inputs=additional_inputs,       
        examples = [
            ["🐍 Write a Python Streamlit program that shows a thumbs up and thumbs down button for scoring an evaluation. When the user clicks, maintain a saved text file that tracks and shows the number of clicks with a refresh and sorts responses by the number of clicks."],
            ["🐍 Write a Python Gradio program that shows a thumbs up and thumbs down button for scoring an evaluation. When the user clicks, maintain a saved text file that tracks and shows the number of clicks with a refresh and sorts responses by the number of clicks."],
            ["📊 Write a Python Streamlit program that creates a Pandas DataFrame and display it using Streamlit. Use emojis to indicate the status of each row (e.g., ✅ for good, ❌ for bad)."],
            ["📊 Write a Python Gradio program that creates a Pandas DataFrame and display it using Streamlit. Use emojis to indicate the status of each row (e.g., ✅ for good, ❌ for bad)."],
            ["🗂 Using Streamlit, create a simple interface where users can upload a CSV file and filter the data based on selected columns."],
            ["🗂 Using Gradio, create a simple interface where users can upload a CSV file and filter the data based on selected columns."],
            ["😃 Implement emoji reactions in a Streamlit app. When a user clicks on an emoji, record the click count in a Pandas DataFrame and display the DataFrame."],
            ["😃 Implement emoji reactions in a Gradio app. When a user clicks on an emoji, record the click count in a Pandas DataFrame and display the DataFrame."],
            ["🔗 Create a program that fetches a dataset from Huggingface Hub and shows basic statistics about it using Pandas in a Streamlit app."],
            ["🔗 Create a program that fetches a dataset from Huggingface Hub and shows basic statistics about it using Pandas in a Gradio app."],
            ["🤖 Use Streamlit to create a user interface for a text summarizer model from Huggingface Hub."],
            ["🤖 Use Gradio to create a user interface for a text summarizer model from Huggingface Hub."],
            ["📈 Create a Streamlit app to visualize time series data. Use Pandas to manipulate the data and plot it using Streamlit’s native plotting options."],
            ["📈 Create a Gradio app to visualize time series data. Use Pandas to manipulate the data and plot it using Streamlit’s native plotting options."],
            ["🎙 Implement a voice-activated feature in a Streamlit interface. Use a pre-trained model from Huggingface Hub for speech recognition."],
            ["🎙 Implement a voice-activated feature in a Gradio interface. Use a pre-trained model from Huggingface Hub for speech recognition."],
            ["🔍 Create a search function in a Streamlit app that filters through a Pandas DataFrame and displays the results."],
            ["🔍 Create a search function in a Gradio app that filters through a Pandas DataFrame and displays the results."],
            ["🤗 Write a Python script that uploads a model to Huggingface Hub and then uses it in a Streamlit app."],
            ["👏 Create a Gradio interface for a clapping hands emoji (👏) counter. When a user inputs a text, the interface should return the number of clapping hands emojis in the text."],
            ["📜 Use Pandas to read an Excel sheet in a Streamlit app. Allow the user to select which sheet they want to view."],
            ["🔒 Implement a login screen in a Streamlit app using Python. Secure the login by hashing the password."],
            ["🤩 Create a Gradio interface that uses a model from Huggingface Hub to generate creative text based on a user’s input. Add sliders for controlling temperature and other hyperparameters."]
        ]
    )
    gr.HTML("""<h2>🤖 Mistral Chat - Gradio 🤖</h2>
        In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬
        Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚
        <h2>🛠 Model Features 🛠</h2>
        <ul>
          <li>🪟 Sliding Window Attention with 128K tokens span</li>
          <li>🚀 GQA for faster inference</li>
          <li>📝 Byte-fallback BPE tokenizer</li>
        </ul>
        <h3>📜 License 📜  Released under Apache 2.0 License</h3>
        <h3>📦 Usage 📦</h3>
        <ul>
          <li>📚 Available on Huggingface Hub</li>
          <li>🐍 Python code snippets for easy setup</li>
          <li>📈 Expected speedups with Flash Attention 2</li>
        </ul>
    """)

    markdown="""
    | Feature | Description | Byline |
    |---------|-------------|--------|
    | 🪟 Sliding Window Attention with 128K tokens span | Enables the model to have a larger context for each token. | Increases model's understanding of context, resulting in more coherent and contextually relevant outputs. |
    | 🚀 GQA for faster inference | Graph Query Attention allows faster computation during inference. | Speeds up the model inference time without sacrificing too much on accuracy. |
    | 📝 Byte-fallback BPE tokenizer | Uses Byte Pair Encoding but can fall back to byte-level encoding. | Allows the tokenizer to handle a wider variety of input text while keeping token size manageable. |
    | 📜 License | Released under Apache 2.0 License | Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. |
    | 📦 Usage | | |
    | 📚 Available on Huggingface Hub | The model can be easily downloaded and set up from Huggingface. | Makes it easier to integrate the model into various projects. |
    | 🐍 Python code snippets for easy setup | Provides Python code snippets for quick and easy model setup. | Facilitates rapid development and deployment, especially useful for prototyping. |
    | 📈 Expected speedups with Flash Attention 2 | Upcoming update expected to bring speed improvements. | Keep an eye out for this update to benefit from performance gains. |

# 🛠 Model Features and More 🛠
## Features
- 🪟 Sliding Window Attention with 128K tokens span  
  - **Byline**: Increases model's understanding of context, resulting in more coherent and contextually relevant outputs.
- 🚀 GQA for faster inference  
  - **Byline**: Speeds up the model inference time without sacrificing too much on accuracy.
- 📝 Byte-fallback BPE tokenizer  
  - **Byline**: Allows the tokenizer to handle a wider variety of input text while keeping token size manageable.
- 📜 License: Released under Apache 2.0 License  
  - **Byline**: Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code.
## Usage 📦
- 📚 Available on Huggingface Hub  
  - **Byline**: Makes it easier to integrate the model into various projects.
- 🐍 Python code snippets for easy setup  
  - **Byline**: Facilitates rapid development and deployment, especially useful for prototyping.
- 📈 Expected speedups with Flash Attention 2  
  - **Byline**: Keep an eye out for this update to benefit from performance gains.
    """
    gr.Markdown(markdown)  
               
    def SpeechSynthesis(result):
        documentHTML5='''
        <!DOCTYPE html>
        <html>
        <head>
            <title>Read It Aloud</title>
            <script type="text/javascript">
                function readAloud() {
                    const text = document.getElementById("textArea").value;
                    const speech = new SpeechSynthesisUtterance(text);
                    window.speechSynthesis.speak(speech);
                }
            </script>
        </head>
        <body>
            <h1>🔊 Read It Aloud</h1>
            <textarea id="textArea" rows="10" cols="80">
        '''
        documentHTML5 = documentHTML5 + result
        documentHTML5 = documentHTML5 + '''
            </textarea>
            <br>
            <button onclick="readAloud()">🔊 Read Aloud</button>
        </body>
        </html>
        '''
        gr.HTML(documentHTML5)
        
    SpeechSynthesis(markdown)
    
demo.queue().launch(debug=True)