Create app.py

app.py

@@ -0,0 +1,206 @@
+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."],
+            ["📊 Create a Pandas DataFrame and display it using Streamlit. Use emojis to indicate the status of each row (e.g., ✅ for good, ❌ for bad)."],
+            ["🗂 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."],
+            ["🔗 Create a program that fetches a dataset from Huggingface Hub and shows basic statistics about it using Pandas in a Streamlit app."],
+            ["🤖 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."],
+            ["🎙 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."],
+            ["🤗 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)
+        # components.html(documentHTML5, width=1280, height=1024)
+        #return result
+    SpeechSynthesis(markdown)
+    
+    
+demo.queue().launch(debug=True)