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README.md

@@ -5,7 +5,7 @@ colorFrom: gray
 colorTo: yellow
 sdk: gradio
 sdk_version: 6.0.2
-app_file: diffusers_disc.py
+app_file: diffusers_empr.py
 pinned: false
 ---
 

diffusers_empr.py

@@ -0,0 +1,87 @@
+import torch
+import gradio as gr
+from PIL import Image
+from diffusers import DiffusionPipeline
+from transformers import pipeline
+
+# -------------------------
+# MODELOS
+# -------------------------
+
+# Diffuser (opcional, solo si quieres usarlo)
+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
+}
+
+# -------------------------
+# FUNCIÓN PRINCIPAL
+# -------------------------
+
+def analyze_skin(image, use_diffuser):
+    if image is None:
+        return None, "No image", "-", "-"
+
+    # Si el usuario quiere usar el diffuser
+    if use_diffuser:
+        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
+
+
+# -------------------------
+# INTERFAZ
+# -------------------------
+
+with gr.Blocks(title="Skin Acne Grade 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")
+            use_diffuser = gr.Checkbox(
+                label="Usar Diffuser (simulación / mejora visual)",
+                value=False
+            )
+            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, use_diffuser],
+        outputs=[output_image, label_out, grade_out, conf_out]
+    )
+
+demo.launch()