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import gradio as gr
from google import genai
from utils import *
from PIL import Image
import os
client = genai.Client(api_key=os.getenv("GEMINI_API_KEY"))
system_instruction = load_verifier_prompt()
generation_config = types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=list[Grading],
seed=1994,
)
def make_inputs(prompt, image):
inputs = []
inputs.extend(prepare_inputs(prompt=prompt, image=image))
return inputs
def format_response(response: dict):
out = ""
for key, value in response.items():
score = f"* **{key}**: {value['score']} (explanation: {value['explanation']})\n"
out += score
return out
def grade(prompt, image):
inputs = make_inputs(prompt, image)
response = client.models.generate_content(
model="gemini-2.0-flash", contents=types.Content(parts=inputs, role="user"), config=generation_config
)
parsed_response = response.parsed[0]
return format_response(parsed_response)
examples = [
["realistic photo a shiny black SUV car with a mountain in the background.", Image.open("car.jpg")],
["photo a green and funny creature standing in front a lightweight forest.", Image.open("green_creature.jpg")],
]
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(
f"""# Grade images with Gemini 2.0 Flash
Following aspects are considered during grading:
* Accuracy to Prompt
* Creativity and Originality
* Visual Quality and Realism
* Consistency and Cohesion
* Emotional or Thematic Resonance
The [system prompt](./verifier_prompt.txt) comes from the paper: [Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Steps](https://arxiv.org/abs/2501.09732).
"""
)
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter the prompt that generated the image to be graded.",
container=False,
)
run_button = gr.Button("Run", scale=0)
image = gr.Image(format="png", type="pil", label="Image", placeholder="The image to be graded.")
result = gr.Markdown(label="Grading Output")
gr.Examples(examples=examples, fn=grade, inputs=[prompt, image], outputs=[result], cache_examples=True)
gr.on(triggers=[run_button.click, prompt.submit], fn=grade, inputs=[prompt, image], outputs=[result])
demo.launch()
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