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import gradio as gr
from google import genai
from google.genai import types
from PIL import Image
from io import BytesIO
import base64
import os
# Initialize the Google Generative AI client with the API key from environment variables
client = genai.Client(api_key=os.environ['GEMINI_API_KEY'])
def generate_item(tag):
# Generate text using Gemini LLM
prompt = f"Generate a short, engaging post about {tag} in the style of a TikTok caption."
text_response = client.models.generate_content(
model='gemini-2.5-flash-preview-04-17',
contents=[prompt]
)
text = text_response.text.strip()
# Generate an image based on the text or tag
image_response = client.models.generate_images(
model='imagen-3.0-generate-002',
prompt=text, # Using the generated text as the prompt
config=types.GenerateImagesConfig(
number_of_images=1,
aspect_ratio="9:16",
person_generation="DONT_ALLOW"
)
)
# Check if images were generated
if image_response.generated_images and len(image_response.generated_images) > 0:
generated_image = image_response.generated_images[0]
image = Image.open(BytesIO(generated_image.image.image_bytes))
else:
# Fallback to a placeholder image if no images are generated
image = Image.new('RGB', (300, 533), color='gray') # Size matches 9:16 aspect ratio
# Convert the image to base64
buffered = BytesIO()
image.save(buffered, format="PNG")
img_str = base64.b64encode(buffered.getvalue()).decode()
return {'text': text, 'image_base64': img_str}
def start_feed(tag):
"""
Start a new feed with the given tag by generating one initial item.
Args:
tag (str): The tag to generate content for.
Returns:
tuple: (current_tag, feed_items, html_content)
"""
item = generate_item(tag)
feed_items = [item]
html_content = generate_html(feed_items)
return tag, feed_items, html_content
def load_more(current_tag, feed_items):
"""
Append a new item to the existing feed using the current tag.
Args:
current_tag (str): The tag currently being used for the feed.
feed_items (list): The current list of feed items.
Returns:
tuple: (current_tag, updated_feed_items, updated_html_content)
"""
new_item = generate_item(current_tag)
feed_items.append(new_item)
html_content = generate_html(feed_items)
return current_tag, feed_items, html_content
def generate_html(feed_items):
"""
Generate an HTML string to display the feed items.
Args:
feed_items (list): List of dictionaries containing 'text' and 'image_base64'.
Returns:
str: HTML string representing the feed.
"""
html_str = '<div style="max-height: 600px; overflow-y: auto; border: 1px solid #ccc; padding: 10px;">'
for item in feed_items:
html_str += f"""
<div style="margin-bottom: 20px; border-bottom: 1px solid #eee; padding-bottom: 20px;">
<p style="font-size: 16px; margin-bottom: 10px;">{item['text']}</p>
<img src="data:image/png;base64,{item['image_base64']}" style="width: 100%; max-width: 300px; height: auto;">
</div>
"""
html_str += '</div>'
return html_str
# Define the Gradio interface
with gr.Blocks(title="TikTok-Style Infinite Feed") as demo:
# Header
gr.Markdown("# TikTok-Style Infinite Feed Generator")
gr.Markdown("Enter a tag or select a suggested one to generate a scrollable feed of AI-generated content!")
# Input components
with gr.Row():
suggested_tags = gr.Dropdown(
choices=["technology", "nature", "art", "food"],
label="Suggested Tags",
value="nature" # Default value
)
tag_input = gr.Textbox(label="Enter a Custom Tag", value="nature")
# Buttons
with gr.Row():
start_button = gr.Button("Start Feed")
load_more_button = gr.Button("Load More")
# Output display
feed_html = gr.HTML(label="Your Feed")
# State variables to maintain feed and tag
current_tag = gr.State(value="")
feed_items = gr.State(value=[])
# Event handlers
def set_tag(selected_tag):
"""Update the tag input when a suggested tag is selected."""
return selected_tag
suggested_tags.change(fn=set_tag, inputs=suggested_tags, outputs=tag_input)
start_button.click(
fn=start_feed,
inputs=tag_input,
outputs=[current_tag, feed_items, feed_html]
)
load_more_button.click(
fn=load_more,
inputs=[current_tag, feed_items],
outputs=[current_tag, feed_items, feed_html]
)
# Launch the app
demo.launch() |