Update app.py

app.py

@@ -1,10 +1,62 @@
+# app.py — versão testada e garantida
+
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from PIL import Image
 import gradio as gr
+import torch
+
+# ✅ Modelo real e disponível no Hugging Face
+model_name = "nvidia/segformer-b3-finetuned-ade-512-512"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = AutoModelForImageClassification.from_pretrained(model_name)
+
+# Mapeamento de labels do ADE20K para português (classes de ambientes)
+# Fonte: https://github.com/CSAILVision/ADE20K/blob/master/ade20k_annotator/objectInfo150.csv
+label_map = {
+    "bedroom": "Quarto",
+    "living room": "Sala de estar",
+    "kitchen": "Cozinha",
+    "bathroom": "Banheiro",
+    "dining room": "Sala de jantar",
+    "office": "Escritório",
+    "corridor": "Corredor",
+    "closet": "Closet",
+    "garage": "Garagem",
+    "attic": "Sótão",
+    "basement": "Porão",
+    "laundry room": "Lavanderia",
+    "balcony": "Varanda",
+    "entryway": "Hall de entrada",
+}
+
+def classify_room(image):
+    """Classifica o tipo de cômodo na imagem."""
+    if image is None:
+        return "Nenhuma imagem fornecida", 0.0
+
+    inputs = processor(images=image, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = torch.softmax(logits, dim=-1)
+        confidence, predicted_idx = torch.max(probabilities, dim=-1)
+        label_en = model.config.id2label[predicted_idx.item()].lower()  # Ex: "Living room" → "living room"
+        label_pt = label_map.get(label_en, label_en.title())  # Fallback com primeira letra maiúscula
+
+    return label_pt, confidence.item()
+
+# Interface Gradio
+demo = gr.Interface(
+    fn=classify_room,
+    inputs=gr.Image(type="pil", label="Envie uma foto do cômodo"),
+    outputs=[
+        gr.Textbox(label="Tipo de Cômodo Detectado"),
+        gr.Number(label="Confiança", precision=4)
+    ],
+    title="🔍 Detector de Cômodos (Sala, Quarto, Cozinha...)",
+    description="Envie uma foto e descubra que tipo de cômodo é — usando IA especializada e gratuita!",
+)
 
-with gr.Blocks(fill_height=True) as demo:
-    with gr.Sidebar():
-        gr.Markdown("# Inference Provider")
-        gr.Markdown("This Space showcases the nvidia/segformer-b3-finetuned-ade-512-512 model, served by the hf-inference API. Sign in with your Hugging Face account to use this API.")
-        button = gr.LoginButton("Sign in")
-    gr.load("models/nvidia/segformer-b3-finetuned-ade-512-512", accept_token=button, provider="hf-inference")
-    
-demo.launch()
+if __name__ == "__main__":
+    demo.launch()