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import gradio as gr
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from search import search_similarity, process_image_for_encoder_gradio
from utils import str_to_bytes
from io import BytesIO
def add_ranking_number(image, rank):
"""Añade un número de ranking a la imagen"""
img_with_rank = image.copy()
draw = ImageDraw.Draw(img_with_rank)
width, height = image.size
circle_radius = min(width, height) // 15
circle_position = (circle_radius + 10, circle_radius + 10)
draw.ellipse(
[(circle_position[0] - circle_radius, circle_position[1] - circle_radius),
(circle_position[0] + circle_radius, circle_position[1] + circle_radius)],
fill='white',
outline='black'
)
font_size = circle_radius
try:
font = ImageFont.truetype("Arial.ttf", font_size)
except:
font = ImageFont.load_default()
text = str(rank + 1)
text_bbox = draw.textbbox((0, 0), text, font=font)
text_width = text_bbox[2] - text_bbox[0]
text_height = text_bbox[3] - text_bbox[1]
text_position = (
circle_position[0] - text_width // 2,
circle_position[1] - text_height // 2
)
draw.text(text_position, text, fill='black', font=font)
return img_with_rank
def process_image_result(image_str, rank):
"""Convierte una cadena de imagen en un objeto PIL Image con ranking"""
try:
img = Image.open(BytesIO(str_to_bytes(image_str)))
return add_ranking_number(img, rank)
except Exception as e:
print(f"Error procesando imagen: {e}")
return None
def interface_fn(mode, input_text, input_image, top_k):
try:
# Determinar qué input usar basado en el modo
if mode == "text":
if not input_text.strip():
return [], "Please, add the description of the clothes to search."
input_data = input_text
else: # mode == "image"
if input_image is None:
return [], "Please, select an image"
input_data = process_image_for_encoder_gradio(input_image, is_bytes=False)
# Show the input data
print(f"Input data: {input_data}") # Para debugging
# Realizar la búsqueda
results = search_similarity(input_data, mode, int(top_k))
# Formatear resultados según el modo
if mode == "text": # Devuelve imágenes
processed_images = []
# Si results es una lista de listas, la aplanamos
if results and isinstance(results[0], list):
print("Recibida lista de listas, aplanando...") # Para debugging
results = [item for sublist in results for item in sublist]
for idx, img_str in enumerate(results):
img = process_image_result(img_str, idx)
if img is not None:
processed_images.append(img)
if not processed_images:
return [], "No se pudieron procesar las imágenes"
return processed_images, None
else: # mode == "image" - Devuelve textos
if isinstance(results, list):
numbered_texts = [f"{i+1}. {text}" for i, text in enumerate(results)]
return [], "\n\n".join(numbered_texts)
else:
return [], str(results)
except Exception as e:
print(f"Error en interface_fn: {str(e)}")
print(f"Tipo de resultados: {type(results)}") # Para debugging
return [], f"Error durante la búsqueda: {str(e)}"
def search_text(input_text, top_k):
try:
if not input_text.strip():
return []
# Realizar la búsqueda
results = search_similarity(input_text, "text", int(top_k))
processed_images = []
# Si results es una lista de listas, la aplanamos
if results and isinstance(results[0], list):
results = [item for sublist in results for item in sublist]
for idx, img_str in enumerate(results):
img = process_image_result(img_str, idx)
if img is not None:
processed_images.append(img)
return processed_images
except Exception as e:
print(f"Error en search_text: {str(e)}")
return []
with gr.Blocks() as demo:
gr.Markdown("# Image search engine based on descriptions (CLIP model)")
with gr.Row():
with gr.Column(scale=1):
input_text = gr.Textbox(
label="Search text",
placeholder="Insert your description here...",
lines=3
)
top_k = gr.Slider(
minimum=1,
maximum=20,
value=5,
step=1,
label="Number of images",
info="How many images do you want to see?"
)
search_button = gr.Button("Search")
with gr.Column(scale=1):
output_gallery = gr.Gallery(
label="Similar images",
columns=3,
height="auto"
)
search_button.click(
fn=search_text,
inputs=[input_text, top_k],
outputs=output_gallery
)
if __name__ == "__main__":
from multiprocessing import freeze_support
freeze_support()
demo.launch() |