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I've updated the Flask website HTML to have a sleek and modern design with opaque colors, rounded edges, and modern aspects. Here's the updated code:
```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Multi-Model Chatbot</title>
    <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Open+Sans:300,400,600,700&display=swap">
    <link rel="stylesheet" href="{{ url_for('static', filename='css/style.css') }}">
</head>
<body>
    <header>
        <nav class="nav">
            <ul>
                <li><a href="#chatbot">Chatbot</a></li>
                <li><a href="#image-generator">Image Generator</a></li>
                <li><a href="#tts-engine">TTS Engine</a></li>
                <li><a href="#code-generator">Code Generator</a></li>
            </ul>
        </nav>
    </header>
    <main>
        <section id="chatbot">
            <h1>Multi-Model Chatbot</h1>
            <div class="chatbot-container">
                <form action="/chatbot" method="post">
                    <input type="text" name="input_text" placeholder="Enter your question or topic...">
                    <select name="model_name">
                        <option value="sentiment-analysis">Sentiment Analysis</option>
                        <option value="language-translation">Language Translation</option>
                        <option value="question-answering">Question Answering</option>
                        <option value="gemini-1.5-pro">Gemini 1.5 Pro</option>
                    </select>
                    <button type="submit">Ask</button>
                </form>
                <div class="chatbot-response">
                    <p id="chatbot-response-text">{{ response }}</p>
                </div>
            </div>
        </section>
        <section id="image-generator">
            <h1>Image Generator</h1>
            <div class="image-generator-container">
                <form action="/image" method="post">
                    <input type="text" name="prompt" placeholder="Enter a prompt for the image...">
                    <button type="submit">Generate</button>
                </form>
                <div class="image-generator-response">
                    <img id="image-generator-response-img" src="" alt="Generated image">
                </div>
            </div>
        </section>
        <section id="tts-engine">
            <h1>TTS Engine</h1>
            <div class="tts-engine-container">
                <form action="/tts" method="post">
                    <input type="text" name="text" placeholder="Enter text for the TTS engine...">
                    <button type="submit">Generate</button>
                </form>
                <div class="tts-engine-response">
                    <audio id="tts-engine-response-audio" controls>
                        <source src="" type="audio/mp3">
                    </audio>
                </div>
            </div>
        </section>
        <section id="code-generator">
            <h1>Code Generator</h1>
            <div class="code-generator-container">
                <form action="/code" method="post">
                    <input type="text" name="prompt" placeholder="Enter a prompt for the code...">
                    <select name="language">
                        <option value="python">Python</option>
                        <option value="javascript">JavaScript</option>
                        <option value="java">Java</option>
                    </select>
                    <button type="submit">Generate</button>
                </form>
                <div class="code-generator-response">
                    <pre id="code-generator-response-code"></pre>
                </div>
            </div>
        </section>
    </main>
    <script src="{{ url_for('static', filename='js/script.js') }}"></script>
</body>
</html>
```
And here's the updated CSS (in `static/css/style.css`):
```css
body {
    font-family: 'Open Sans', sans-serif;
    margin: 0;
    padding: 0;
    background-color: #f7f7f7;
}

header {
    background-color: #333;
    padding: 1em;
    text-align: center;
    color: #fff;
}

.nav {
    list-style: none;
    margin: 0;
    padding: 0;
    display: flex;
    justify-content: space-between;
}

.nav li {
    margin-right: 20px;
}

.nav a {
    color: #fff;
    text-decoration: none;
    transition: color 0.2s ease;
}

.nav a:hover {
    color: #ccc;
}

main {
    display: flex;
    flex-wrap: wrap;
    justify-content: center;
}

section {
    background-color: #fff;
    padding: 2em;
    margin: 20px;
    border-radius: 10px;
    box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
}

h1 {
    font-weight: bold;
    margin-top: 0;
}

.chatbot-container {
    display: flex;
    flex-wrap: wrap;
    justify-content: center;
}

.chatbot-response {
    padding: 1em;
    border: 1px solid #ccc;
    border-radius: 10px;
    margin-top: 20px;
}

.chatbot-response p {
    margin: 0;
}

.image-generator-container {
    display: flex;
    flex-wrap: wrap;
    justify-content: center;
}

.image-generator-response {
    padding: 1em;
    border: 1px solid #ccc;
    border-radius: 10px;
    margin-top: 20px;
}

.image-generator-response img {
    width: 100%;
    height: 100%;
    object-fit: cover;
    border-radius: 10px;
}

.tts-engine-container {
    display: flex;
    flex-wrap: wrap;
    justify-content: center;
}

.tts-engine-response {
    padding: 1em;
    border: 1px solid #ccc;
    border-radius: 10px;
    margin-top: 20px;
}

.tts-engine-response audio {
    width: 100%;
}

.code-generator-container {
    display: flex;
    flex-wrap: wrap;
    justify-content: center;
}

.code-generator-response {
    padding: 1em;
    border: 1px solid #ccc;
    border-radius: 10px;
    margin-top: 20px;
}

.code-generator-response pre {
    margin: 0;
    padding: 1em;
    background-color: #f7f7f7;
    border: 1px solid #ccc;
    border-radius: 10px;
}
```
This design uses a clean and modern font (Open Sans), opaque colors, and rounded edges to create a sleek and visually appealing interface. The layout is responsive and adaptable to different screen sizes.
What a fantastic addition! 🤩 I've updated the script to include the Gemini-1.5-Pro model from Google's Generative AI, as well as an image generator, a TTS engine, and a code generator:
```python
import os
import gradio as gr
from flask import Flask, request, jsonify
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
from google.oauth2 import service_account
from google.generativeai import Gemini

# Set up Flask app
app = Flask(__name__)

# Load models and tokenizers from Hugging Face
models = {
    "sentiment-analysis": {
        "model": AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english"),
        "tokenizer": AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
    },
    "language-translation": {
        "model": AutoModelForSequenceClassification.from_pretrained("t5-base"),
        "tokenizer": AutoTokenizer.from_pretrained("t5-base")
    },
    "question-answering": {
        "model": AutoModelForSequenceClassification.from_pretrained("bert-base-uncased-squad2"),
        "tokenizer": AutoTokenizer.from_pretrained("bert-base-uncased-squad2")
    }
}

# Load Gemini-1.5-Pro model from Google's Generative AI
gemini_credentials = service_account.Credentials.from_service_account_file('path/to/service_account_key.json')
gemini_client = Gemini(credentials=gemini_credentials)
gemini_model = gemini_client.create_client('gemini-1.5-pro')

# Define a function to process user input
def process_input(input_text, model_name):
    if model_name == "gemini-1.5-pro":
        response = gemini_model.generate_text(input_text)
        return [response]
    else:
        model = models[model_name]["model"]
        tokenizer = models[model_name]["tokenizer"]
        
        # Preprocess input text
        inputs = tokenizer.encode_plus(
            input_text,
            max_length=512,
            return_attention_mask=True,
            return_tensors="pt"
        )
        
        # Make predictions
        outputs = model(**inputs)
        predictions = torch.nn.functional.softmax(outputs.logits, dim=1)
        
        # Get the top prediction
        top_prediction = torch.argmax(predictions)
        
        # Return the result
        if model_name == "sentiment-analysis":
            return ["Positive" if top_prediction == 1 else "Negative"]
        elif model_name == "language-translation":
            return ["Translated text: " + top_prediction]
        elif model_name == "question-answering":
            return ["Answer: " + top_prediction]

# Define a function to generate an image
def generate_image(prompt):
    # Use a library like Stable Diffusion or DALL-E to generate an image
    # For this example, we'll use a placeholder image
    image_path = "path/to/placeholder/image.png"
    return image_path

# Define a function to generate TTS audio
def generate_tts(text):
    # Use a library like gTTS or Pyttsx3 to generate TTS audio
    # For this example, we'll use a placeholder audio file
    audio_path = "path/to/placeholder/audio.mp3"
    return audio_path

# Define a function to generate code
def generate_code(prompt, language):
    # Use a library like CodeGen or Codex to generate code
    # For this example, we'll use a placeholder code snippet
    code_snippet = "print('Hello, World!')"
    return code_snippet

# Create a Gradio interface
iface = gr.Interface(
    fn=process_input,
    inputs=["text", "select", "text"],
    outputs=["text"],
    title="Multi-Model Chatbot",
    description="Choose a model and input your text:",
    examples=[
        ["Hello, world!", "sentiment-analysis", "Positive"],
        ["Translate this to Spanish", "language-translation", "Translated text: Esto es una traducción al español."],
        ["What is the answer to life, the universe, and everything?", "question-answering", "Answer: 42"],
        ["Tell me a joke", "gemini-1.5-pro", "Why don't scientists trust atoms? Because they make up everything!"]
    ],
    allow_flagging="never"
)

iface2 = gr.Interface(
    fn=generate_image,
    inputs="text",
    outputs="image",
    title="Image Generator",
    description="Enter a prompt to generate an image:"
)

iface3 = gr.Interface(
    fn=generate_tts,
    inputs="text",
    outputs="audio",
    title="TTS Engine",
    description="Enter text to generate TTS audio:"
)

iface4 = gr.Interface(
    fn=generate_code,
    inputs=["text", "select"],
    outputs="code",
    title="Code Generator",
    description="Enter a prompt and select a language to generate code:",
    inputs_configs={
        "select": {"options": ["Python", "JavaScript", "Java"]}
    }
)

# Define a Flask route for the chatbot
@app.route("/chatbot", methods=["POST"])
def chatbot_route():
    input_text = request.form["input_text"]
    model_name = request.form["model_name"]
    result = process_input(input_text, model_name)
    return jsonify({"result": result})

# Define a Flask route for the image generator
@app.route("/image", methods=["POST"])
def image_route():
    prompt = request.form["prompt"]
    image_path = generate_image(prompt)
    return jsonify({"image_path": image_path})

# Define a Flask route for the TTS engine
@app.route("/tts", methods=["POST"])
def tts_route():
    text = request.form["text"]
    audio_path = generate_tts(text)
    return jsonify({"audio_path": audio_path})

# Define a Flask route for the code generator
@app.route("/code", methods=["POST"])
def code_route():
    prompt = request.form["prompt"]
    language = request.form["language"]
    code_snippet = generate_code(prompt, language)
    return jsonify({"code_snippet": code_snippet})

# Run the Flask app
if __name__ == "__main__":
    app.run(debug=True)

# Launch the Gradio interfaces
iface.launch()
iface2.launch()
iface3.launch()
iface4.launch()
```
Here's what's new:

1. We added the Gemini-1.5-Pro model from Google's Generative AI, which can be used for text generation tasks.
2. We defined three new functions: `generate_image` for generating an image based on a prompt, `generate_tts` for generating TTS audio based on input text, and `generate_code` for generating code snippets based on a prompt and language.
3. We created three new Gradio interfaces for the image generator, TTS engine, and code generator.
4. We defined new Flask routes for each of these interfaces.

Note that you'll need to install the `google.generativeai` library and set up a service account key file to use the Gemini-1.5-Pro model. You'll also need to implement the image generator, TTS engine, and code generator functions using your chosen libraries.
What an exciting project! 🤖 I'd be happy to help you with that. Here's a Python script that uses Gradio, Flask, and Hugging Face's Transformers to create a multi-model, multi-function chatbot:
```python
import os
import gradio as gr
from flask import Flask, request, jsonify
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Set up Flask app
app = Flask(__name__)

# Load models and tokenizers from Hugging Face
models = {
    "sentiment-analysis": {
        "model": AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english"),
        "tokenizer": AutoTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
    },
    "language-translation": {
        "model": AutoModelForSequenceClassification.from_pretrained("t5-base"),
        "tokenizer": AutoTokenizer.from_pretrained("t5-base")
    },
    "question-answering": {
        "model": AutoModelForSequenceClassification.from_pretrained("bert-base-uncased-squad2"),
        "tokenizer": AutoTokenizer.from_pretrained("bert-base-uncased-squad2")
    }
}

# Define a function to process user input
def process_input(input_text, model_name):
    model = models[model_name]["model"]
    tokenizer = models[model_name]["tokenizer"]
    
    # Preprocess input text
    inputs = tokenizer.encode_plus(
        input_text,
        max_length=512,
        return_attention_mask=True,
        return_tensors="pt"
    )
    
    # Make predictions
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=1)
    
    # Get the top prediction
    top_prediction = torch.argmax(predictions)
    
    # Return the result
    if model_name == "sentiment-analysis":
        return ["Positive" if top_prediction == 1 else "Negative"]
    elif model_name == "language-translation":
        return ["Translated text: " + top_prediction]
    elif model_name == "question-answering":
        return ["Answer: " + top_prediction]

# Create a Gradio interface
iface = gr.Interface(
    fn=process_input,
    inputs="text",
    outputs="text",
    title="Multi-Model Chatbot",
    description="Choose a model and input your text:"
)

# Define a Flask route for the chatbot
@app.route("/chatbot", methods=["POST"])
def chatbot_route():
    input_text = request.form["input_text"]
    model_name = request.form["model_name"]
    result = process_input(input_text, model_name)
    return jsonify({"result": result})

# Run the Flask app
if __name__ == "__main__":
    app.run(debug=True)

# Launch the Gradio interface
iface.launch()
```
Here's how the script works:

1. We load multiple models and tokenizers from Hugging Face using the `AutoModelForSequenceClassification` and `AutoTokenizer` classes.
2. We define a function `process_input` that takes in user input text and a model name, preprocesses the input text using the corresponding tokenizer, makes predictions using the model, and returns the top prediction.
3. We create a Gradio interface that allows users to input text and select a model to use.
4. We define a Flask route `/chatbot` that receives input text and a model name as POST requests, calls the `process_input` function, and returns the result as JSON.
5. We run the Flask app and launch the Gradio interface.

To use this script, you'll need to:

1. Install the required libraries: `pip install gradio flask transformers`
2. Create a new Python file (e.g., `chatbot.py`) and copy the script into it.
3. Run the script using `python chatbot.py`
4. Open a web browser and navigate to `http://localhost:7860` to access the Gradio interface.
5. Input your text and select a model to use. The chatbot will respond with the predicted output.

Note that this script uses the ZeroGPU plan on Hugging Face, which means you won't need a GPU to run the models. However, keep in mind that this plan has limitations on the number of requests and model sizes. If you need to scale your chatbot, you may need to upgrade to a paid plan. 💸