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prueba / diffusers_empr.py

Apedlop

e98c523 11 months ago

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import torch
import gradio as gr
from PIL import Image
from diffusers import DiffusionPipeline
from transformers import pipeline

# Diffuser
# diffuser = DiffusionPipeline.from_pretrained(
#     "bb1070/catvton-acne-4000-full",
#     torch_dtype=torch.float16
# ).to("cuda")

# Clasificador de acné (GRADE REAL)
classifier = pipeline(
    "image-classification",
    model="imfarzanansari/skintelligent-acne",
    device=0
)

GRADE_MAP = {
    "Clear Skin": 0,
    "Mild Acne": 1,
    "Moderate Acne": 2,
    "Severe Acne": 3
}

# def analyze_skin(image, use_diffuser):
#     if image is None:
#         return None, "No image", "-", "-"

#     prompt = "face with acne, dermatology photo, neutral lighting"
#     image = diffuser(prompt, image=image).images[0]

#     # Clasificación
#     result = classifier(image)
#     top = result[0]

#     label = top["label"]
#     confidence = round(top["score"], 3)
#     grade = GRADE_MAP.get(label, "N/A")

#     return image, label, grade, confidence

with gr.Blocks(title="Skin Acne AI") as demo:
    gr.Markdown("## Análisis de Acné con IA")
    gr.Markdown(
        "Sube una imagen de la piel. "
        "El sistema devuelve un **GRADE dermatológico no clínico**."
    )

    with gr.Row():
        with gr.Column():
            input_image = gr.Image(type="pil", label="Imagen de la piel")
            btn = gr.Button("Analizar")

        with gr.Column():
            output_image = gr.Image(label="Imagen procesada")
            label_out = gr.Textbox(label="Diagnóstico IA")
            grade_out = gr.Textbox(label="Grade (0–3)")
            conf_out = gr.Textbox(label="Confianza")

    btn.click(
        # analyze_skin,
        inputs=[input_image],
        outputs=[output_image, label_out, grade_out, conf_out]
    )

demo.launch()

	import torch
	import gradio as gr
	from PIL import Image
	from diffusers import DiffusionPipeline
	from transformers import pipeline

	# Diffuser
	# diffuser = DiffusionPipeline.from_pretrained(
	# "bb1070/catvton-acne-4000-full",
	# torch_dtype=torch.float16
	# ).to("cuda")

	# Clasificador de acné (GRADE REAL)
	classifier = pipeline(
	"image-classification",
	model="imfarzanansari/skintelligent-acne",
	device=0
	)

	GRADE_MAP = {
	"Clear Skin": 0,
	"Mild Acne": 1,
	"Moderate Acne": 2,
	"Severe Acne": 3
	}

	# def analyze_skin(image, use_diffuser):
	# if image is None:
	# return None, "No image", "-", "-"

	# prompt = "face with acne, dermatology photo, neutral lighting"
	# image = diffuser(prompt, image=image).images[0]

	# # Clasificación
	# result = classifier(image)
	# top = result[0]

	# label = top["label"]
	# confidence = round(top["score"], 3)
	# grade = GRADE_MAP.get(label, "N/A")

	# return image, label, grade, confidence

	with gr.Blocks(title="Skin Acne AI") as demo:
	gr.Markdown("## Análisis de Acné con IA")
	gr.Markdown(
	"Sube una imagen de la piel. "
	"El sistema devuelve un GRADE dermatológico no clínico."
	)

	with gr.Row():
	with gr.Column():
	input_image = gr.Image(type="pil", label="Imagen de la piel")
	btn = gr.Button("Analizar")

	with gr.Column():
	output_image = gr.Image(label="Imagen procesada")
	label_out = gr.Textbox(label="Diagnóstico IA")
	grade_out = gr.Textbox(label="Grade (0–3)")
	conf_out = gr.Textbox(label="Confianza")

	btn.click(
	# analyze_skin,
	inputs=[input_image],
	outputs=[output_image, label_out, grade_out, conf_out]
	)

	demo.launch()