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import json
import os
import time
import uuid
import tempfile
from PIL import Image
import gradio as gr
import base64
import mimetypes
from google import genai
from google.genai import types
def save_binary_file(file_name, data):
with open(file_name, "wb") as f:
f.write(data)
def generate(text, file_name, api_key, model="gemini-2.0-flash-exp"):
# Initialize client using provided api_key (or fallback to env variable)
client = genai.Client(api_key=(api_key.strip() if api_key and api_key.strip() != ""
else os.environ.get("GEMINI_API_KEY")))
files = [
client.files.upload(file=file_name),
]
contents = [
types.Content(
role="user",
parts=[
types.Part.from_uri(
file_uri=files[0].uri,
mime_type=files[0].mime_type,
),
types.Part.from_text(text=text),
],
),
]
generate_content_config = types.GenerateContentConfig(
temperature=1,
top_p=0.95,
top_k=40,
max_output_tokens=8192,
response_modalities=[
"image",
"text",
],
response_mime_type="text/plain",
)
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
temp_path = tmp.name
for chunk in client.models.generate_content_stream(
model=model,
contents=contents,
config=generate_content_config,
):
if not chunk.candidates or not chunk.candidates[0].content or not chunk.candidates[0].content.parts:
continue
inline_data = chunk.candidates[0].content.parts[0].inline_data
if inline_data:
save_binary_file(temp_path, inline_data.data)
print(
"File of mime type "
f"{inline_data.mime_type} saved to: {temp_path} and prompt input :{text}"
)
else:
print(chunk.text)
del files
return temp_path
def process_image_and_prompt(composite_pil, prompt, gemini_api_key):
try:
# Save the composite image to a temporary file.
with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
composite_path = tmp.name
composite_pil.save(composite_path)
file_name = composite_path
input_text = prompt
model = "gemini-2.0-flash-exp"
gemma_edited_image_path = generate(text=input_text, file_name=file_name, api_key=gemini_api_key, model=model)
print("image_path ", gemma_edited_image_path)
result_img = Image.open(gemma_edited_image_path)
if result_img.mode == "RGBA":
result_img = result_img.convert("RGB")
return [result_img]
except Exception as e:
raise gr.Error(f"Error Getting {e}", duration=5)
# Build a Blocks-based interface to include the custom HTML header.
with gr.Blocks() as demo:
# HTML Header for the application.
gr.HTML(
"""
<div style='display: flex; align-items: center; justify-content: center; gap: 20px'>
<div style="background-color: var(--block-background-fill); border-radius: 8px">
<img src="https://www.gstatic.com/lamda/images/gemini_favicon_f069958c85030456e93de685481c559f160ea06b.png" style="width: 100px; height: 100px;">
</div>
<div>
<h1>Gen AI Image Editing</h1>
<p>Gemini using for Image Editing</p>
<p>Powered by <a href="https://gradio.app/">Gradio</a> ⚡️</p>
<p>Get an API Key <a href="https://aistudio.google.com/apikey">here</a></p>
<p>Follow me on Twitter: <a href="https://x.com/Ameerazam18">Ameerazam18</a></p>
</div>
</div>
"""
)
# Title and description.
# Define examples to be shown within the Gradio interface
examples = [
# Each example is a list corresponding to the inputs:
# [Input Image, Prompt, Guidance Scale, Number of Steps, LoRA Name]
["data/1.webp", 'change text to "AMEER"'],
["data/2.webp", "remove the spoon from hand only"],
["data/3.webp", 'change text to "Make it "'],
["data/1.jpg", "add joker style only on face"],
["data/1777043.jpg", "add joker style only on face"],
["data/2807615.jpg","add lipstick on lip only "],
["data/76860.jpg", "add lipstick on lip only "],
["data/2807615.jpg", "make it happy looking face only"],
]
gr.Markdown("Upload an image and enter a prompt to generate outputs in the gallery. Do not Use NFSW Images")
with gr.Row():
with gr.Column():
image_input = gr.Image(
type="pil",
label="Upload Image",
image_mode="RGBA"
)
gemini_api_key = gr.Textbox(
lines=1,
placeholder="Enter Gemini API Key (optional)",
label="Gemini API Key (optional) Generate and fill here"
)
prompt_input = gr.Textbox(
lines=2,
placeholder="Enter prompt here...",
label="Prompt"
)
submit_btn = gr.Button("Generate")
with gr.Column():
output_gallery = gr.Gallery(label="Generated Outputs")
# Set up the interaction.
submit_btn.click(
fn=process_image_and_prompt,
inputs=[image_input, prompt_input, gemini_api_key],
outputs=output_gallery,
)
gr.Examples(
examples=examples,
inputs=[image_input, prompt_input, gemini_api_key],
label="Try these examples"
)
demo.queue(max_size=500).launch()