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import gradio as gr
from transformers import AutoModelForCausalLM, AutoProcessor
import torch
from PIL import Image
import io
# Load model and processor (using CPU)
folder_path = "diffusers/shot-categorizer-v0"
model = AutoModelForCausalLM.from_pretrained(folder_path, trust_remote_code=True).eval()
processor = AutoProcessor.from_pretrained(folder_path, trust_remote_code=True)
# Define analysis function
def analyze_image(image):
# Convert Gradio image input to PIL Image
if isinstance(image, Image.Image):
img = image.convert("RGB")
else:
img = Image.open(io.BytesIO(image)).convert("RGB")
prompts = ["<COLOR>", "<LIGHTING>", "<LIGHTING_TYPE>", "<COMPOSITION>"]
results = {}
# Process each prompt
with torch.no_grad():
for prompt in prompts:
inputs = processor(text=prompt, images=img, return_tensors="pt")
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
early_stopping=False,
do_sample=False,
num_beams=3,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(
generated_text, task=prompt, image_size=(img.width, img.height)
)
results[prompt] = parsed_answer
# Format the output
output_text = "Image Analysis Results:\n\n"
output_text += f"Color: {results['<COLOR>']}\n"
output_text += f"Lighting: {results['<LIGHTING>']}\n"
output_text += f"Lighting Type: {results['<LIGHTING_TYPE>']}\n"
output_text += f"Composition: {results['<COMPOSITION>']}\n"
return output_text
# Create Gradio interface
with gr.Blocks(title="Image Analyzer") as demo:
gr.Markdown("# Image Analysis Demo")
gr.Markdown("Upload an image to analyze its color, lighting, and composition characteristics.")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil", label="Upload Image")
analyze_button = gr.Button("Analyze Image")
with gr.Column():
output_text = gr.Textbox(label="Analysis Results", lines=10)
# Add example images
examples = gr.Examples(
examples=["shot.jpg"],
inputs=image_input,
label="Try with this example"
)
# Connect the button to the function
analyze_button.click(
fn=analyze_image,
inputs=image_input,
outputs=output_text
)
# Launch the demo
demo.launch() |