Update app.py

app.py

@@ -4,11 +4,15 @@ from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassifica
 from diffusers import StableDiffusionPipeline
 import torch
 import numpy as np
+import random
 from datasets import Dataset
 from huggingface_hub import HfApi
 from datetime import datetime, time
 from accelerate import Accelerator
 from accelerate.utils import set_seed
+from PIL import Image
+import io
+import base64
 
 # Set a seed for reproducibility
 set_seed(42)
@@ -47,8 +51,7 @@ def set_sleep_time():
 
 @spaces.GPU
 @torch.no_grad()
-def generate_text():
-    prompt = "This AI-driven website is unique because"
+def generate_text(prompt):
     return text_generator(prompt, max_length=100, num_return_sequences=1)[0]['generated_text']
 
 @spaces.GPU
@@ -66,13 +69,54 @@ def analyze_sentiment(text):
     probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
     return probabilities.cpu().numpy()[0]
 
-def generate_content():
-    text_content = generate_text()
-    image_prompt = "An abstract representation of a unique website"
-    image = generate_image(image_prompt)
-    sentiment = analyze_sentiment(text_content)
+def generate_website_content():
+    # Generate website title
+    title = generate_text("A unique website title:").split(':')[1].strip()
+    
+    # Generate main content
+    content = generate_text("A short paragraph about a random topic:").split(':')[1].strip()
+    
+    # Generate an image
+    image = generate_image("An abstract representation of " + title)
+    
+    # Analyze sentiment of the content
+    sentiment = analyze_sentiment(content)
     sentiment_label = "Positive" if sentiment[1] > sentiment[0] else "Negative"
-    return text_content, image, f"Content Sentiment: {sentiment_label}"
+    
+    # Generate a random color
+    color = "#{:06x}".format(random.randint(0, 0xFFFFFF))
+    
+    # Generate a random layout
+    layouts = ["centered", "left-aligned", "right-aligned"]
+    layout = random.choice(layouts)
+    
+    return title, content, image, sentiment_label, color, layout
+
+def update_website():
+    title, content, image, sentiment, color, layout = generate_website_content()
+    
+    # Convert PIL Image to base64
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    img_str = base64.b64encode(buffered.getvalue()).decode()
+    
+    layout_css = {
+        "centered": "text-align: center;",
+        "left-aligned": "text-align: left;",
+        "right-aligned": "text-align: right;"
+    }
+    
+    html_content = f"""
+    <div style="font-family: Arial, sans-serif; padding: 20px; background-color: {color}; {layout_css[layout]}">
+        <h1>{title}</h1>
+        <p>{content}</p>
+        <img src="data:image/png;base64,{img_str}" alt="Generated Image" style="max-width: 100%; height: auto;">
+        <p>Content Sentiment: {sentiment}</p>
+        <button onclick="document.getElementById('refresh_button').click()">Regenerate Website</button>
+    </div>
+    """
+    
+    return html_content
 
 def save_feedback(feedback, rating):
     feedback_data.append({"text": feedback, "rating": rating})
@@ -88,25 +132,27 @@ def get_gpu_info():
 with gr.Blocks() as demo:
     gr.Markdown("# AI-Driven Dynamic Website")
     gr.Markdown(get_gpu_info())
+    
+    html_output = gr.HTML()
+    refresh_button = gr.Button("Regenerate Website", elem_id="refresh_button")
+    
     with gr.Row():
-        with gr.Column():
-            text_output = gr.Textbox(label="Generated Content")
-            image_output = gr.Image(label="Generated Image")
-            sentiment_output = gr.Textbox(label="Sentiment Analysis")
-        with gr.Column():
-            generate_button = gr.Button("Generate New Content")
-            feedback_input = gr.Textbox(label="Provide Feedback")
-            feedback_rating = gr.Radio(["Positive", "Negative"], label="Rate the content")
-            feedback_button = gr.Button("Submit Feedback")
-            feedback_output = gr.Textbox(label="Feedback Status")
+        feedback_input = gr.Textbox(label="Provide Feedback")
+        feedback_rating = gr.Radio(["Positive", "Negative"], label="Rate the content")
+        feedback_button = gr.Button("Submit Feedback")
+    
+    feedback_output = gr.Textbox(label="Feedback Status")
     
     sleep_button = gr.Button("Set Sleep Time")
     sleep_output = gr.Textbox(label="Sleep Time Status")
     
-    generate_button.click(generate_content, outputs=[text_output, image_output, sentiment_output])
+    refresh_button.click(update_website, outputs=html_output)
     feedback_button.click(save_feedback, inputs=[feedback_input, feedback_rating], outputs=feedback_output)
     sleep_button.click(set_sleep_time, outputs=sleep_output)
 
+# Initialize the website on startup
+demo.load(update_website, outputs=html_output)
+
 # Set sleep time when the app starts
 set_sleep_time()