app.py

import gradio as gr
from transformers import pipeline
import numpy as np
from PIL import Image
import io
import base64

# Initialize sentiment analysis pipeline (lightweight for CPU)
sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

# Mock text-to-image function (CPU-friendly)
def generate_mock_image(text_prompt, width=200, height=200):
    img_array = np.zeros((height, width, 3), dtype=np.uint8)
    for i in range(height):
        for j in range(width):
            img_array[i, j] = [(i % 255), (j % 255), ((i + j) % 255)]  # RGB gradient
    img = Image.fromarray(img_array)
    buffered = io.BytesIO()
    img.save(buffered, format="PNG")
    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
    return f"data:image/png;base64,{img_str}"

# Sentiment analysis function
def analyze_sentiment(text):
    if not text.strip():
        return "Please enter some text."
    result = sentiment_analyzer(text)[0]
    label = result['label']
    score = result['score']
    return f"Sentiment: {label} (Confidence: {score:.2%})"

# Chatbot feedback function
def chatbot_response(user_feedback, chat_history):
    if not user_feedback.strip():
        return chat_history, "Please provide feedback."
    chat_history.append((
        f"**You**: {user_feedback}", 
        f"**Bot**: Thanks for your feedback! I understood: '{user_feedback}'."
    ))
    return chat_history, ""

# Custom CSS for dark grey, minimalist UI
custom_css = """
body, .gradio-container {
    background: #2d2d2d !important;
    color: #d4d4d4 !important;
    font-family: 'Inter', -apple-system, sans-serif;
    margin: 0;
    padding: 20px;
}
.tab-nav button {
    background: #3a3a3a !important;
    color: #a3a3a3 !important;
    border: none !important;
    padding: 12px 20px !important;
    border-radius: 8px 8px 0 0 !important;
    transition: background 0.3s, color 0.3s;
}
.tab-nav button:hover, .tab-nav button[aria-selected="true"] {
    background: #4a4a4a !important;
    color: #e0e0e0 !important;
}
.block, .gr-panel {
    background: #353535 !important;
    border-radius: 10px !important;
    padding: 20px !important;
    box-shadow: 0 4px 10px rgba(0, 0, 0, 0.25);
    margin-bottom: 20px;
}
input, textarea, .gr-textbox {
    background: #2a2a2a !important;
    color: #d4d4d4 !important;
    border: 1px solid #4a4a4a !important;
    border-radius: 8px !important;
    padding: 12px !important;
    transition: border-color 0.2s;
}
input:focus, textarea:focus {
    border-color: #6b6b6b !important;
    outline: none;
}
button {
    background: #4a4a4a !important;
    color: #e0e0e0 !important;
    border: none !important;
    border-radius: 8px !important;
    padding: 12px 24px !important;
    font-weight: 600;
    transition: background 0.2s, transform 0.2s;
}
button:hover {
    background: #5a5a5a !important;
    transform: scale(1.03);
}
.gr-image img {
    border-radius: 8px !important;
    border: 2px solid #4a4a4a !important;
    max-width: 100%;
}
.gr-chatbot .message {
    border-radius: 8px !important;
    padding: 12px !important;
    margin: 8px 0 !important;
}
.gr-chatbot .message:nth-child(odd) {
    background: #3a3a3a !important; /* User messages */
}
.gr-chatbot .message:nth-child(even) {
    background: #2a2a2a !important; /* Bot messages */
}
h1, h2, h3 {
    color: #b3b3b3 !important;
    font-weight: 600;
}
@media (max-width: 768px) {
    .gradio-container {
        padding: 10px;
    }
    .block {
        padding: 15px !important;
    }
    button {
        padding: 10px 20px !important;
    }
    .tab-nav button {
        padding: 10px 15px !important;
        font-size: 14px;
    }
}
"""

# Main Gradio app with Tabs
with gr.Blocks(css=custom_css) as demo:
    gr.Markdown(
        """
        # 🛠️ Interactive AI Dashboard
        Explore **Sentiment Analysis**, **Text-to-Image Generation**, and **Feedback Chatbot** in a sleek grey interface.
        Built for Hugging Face Spaces (free tier, CPU-only).
        """
    )
    
    with gr.Tabs():
        # Sentiment Analysis Tab
        with gr.Tab("Sentiment Analysis"):
            with gr.Row():
                with gr.Column(scale=3):
                    gr.Markdown("### 📝 Analyze Text Sentiment")
                    sentiment_input = gr.Textbox(
                        label="Your Text",
                        placeholder="Enter text like 'This app is awesome!'",
                        lines=4,
                        show_label=False
                    )
                    sentiment_button = gr.Button("Analyze", variant="primary")
                    sentiment_output = gr.Textbox(
                        label="Result",
                        interactive=False,
                        placeholder="Sentiment result will appear here..."
                    )
                with gr.Column(scale=2):
                    gr.Markdown("### Example Prompts")
                    gr.Examples(
                        examples=[
                            "I’m thrilled about this project!",
                            "Today feels a bit gloomy.",
                            "Programming is tough but rewarding!"
                        ],
                        inputs=sentiment_input
                    )
            sentiment_button.click(
                fn=analyze_sentiment,
                inputs=sentiment_input,
                outputs=sentiment_output,
                show_progress=True
            )
        
        # Text-to-Image Tab
        with gr.Tab("Text-to-Image"):
            with gr.Row():
                with gr.Column(scale=3):
                    gr.Markdown("### 🖼️ Generate Mock Images")
                    image_prompt = gr.Textbox(
                        label="Image Prompt",
                        placeholder="Describe an image, e.g., 'Abstract colorful pattern'",
                        lines=3,
                        show_label=False
                    )
                    image_button = gr.Button("Generate", variant="primary")
                    image_output = gr.Image(
                        label="Generated Image",
                        type="pil",
                        interactive=False
                    )
                with gr.Column(scale=2):
                    gr.Markdown("### Info")
                    gr.Markdown(
                        "This mock generator creates gradient images to stay lightweight for the free tier."
                    )
            image_button.click(
                fn=generate_mock_image,
                inputs=image_prompt,
                outputs=image_output,
                show_progress=True
            )
        
        # Chatbot Tab
        with gr.Tab("Feedback Chatbot"):
            with gr.Row():
                with gr.Column():
                    gr.Markdown("### 💬 Share Your Thoughts")
                    chatbot = gr.Chatbot(
                        label="Conversation",
                        bubble_full_width=False,
                        height=400
                    )
                    feedback_input = gr.Textbox(
                        label="Your Message",
                        placeholder="Type your feedback here...",
                        lines=2,
                        show_label=False
                    )
                    feedback_button = gr.Button("Send", variant="primary")
                    feedback_output = gr.Textbox(
                        label="Status",
                        interactive=False,
                        placeholder="Bot response status..."
                    )
            feedback_button.click(
                fn=chatbot_response,
                inputs=[feedback_input, chatbot],
                outputs=[chatbot, feedback_output],
                show_progress=True
            )

# Launch the app
if __name__ == "__main__":
    demo.launch()