Update app.py

app.py

@@ -1,4 +1,4 @@
-# nsfw_app.py
+# nsfw_app_api_only.py
 import gradio as gr
 import torch
 import torch.nn as nn
@@ -13,7 +13,7 @@ import traceback
 labels = ["Drawings", "Hentai", "Neutral", "Porn", "Sexy"]
 theme_color = "#6C5B7B"
 
-# ---------------- MODEL (stesso file pesi) ----------------
+# ---------------- MODEL ----------------
 class Classifier(nn.Module):
     def __init__(self):
         super().__init__()
@@ -38,39 +38,23 @@ preprocess = transforms.Compose([
                          std =[0.229,0.224,0.225])
 ])
 
-# Carica pesi (stesso file che usavi)
 model = Classifier()
 model.load_state_dict(torch.load("classify_nsfw_v3.0.pth", map_location="cpu"))
 model.eval()
 
-# ---------------- FUNZIONE UNICA predict (accetta SOLO base64) ----------------
+# ---------------- FUNZIONE UNICA ----------------
 def predict(base64_input: str):
-    """
-    Unico input dell'API: stringa base64 (es. "data:image/jpeg;base64,...")
-    Ritorna: (label_str, {label:prob})
-    """
     try:
         if not base64_input or not isinstance(base64_input, str):
             return "Input base64 mancante o non valido", {}
 
-        # rimuovi eventuale prefisso data:image...
         if base64_input.startswith("data:image"):
             base64_input = base64_input.split(",", 1)[1]
 
-        # decodifica base64
-        try:
-            img_bytes = base64.b64decode(base64_input)
-        except Exception as e:
-            return f"Errore decodifica base64: {e}", {}
+        img_bytes = base64.b64decode(base64_input)
+        img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
 
-        # apri immagine
-        try:
-            img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
-        except Exception as e:
-            return f"Errore apertura immagine: {e}", {}
-
-        # preprocess + inferenza
-        img_tensor = preprocess(img).unsqueeze(0)  # 1x3x224x224
+        img_tensor = preprocess(img).unsqueeze(0)
         with torch.no_grad():
             logits = model(img_tensor)
             probs = torch.nn.functional.softmax(logits[0], dim=0)
@@ -82,56 +66,45 @@ def predict(base64_input: str):
     except Exception:
         return f"Unhandled error:\n{traceback.format_exc()}", {}
 
-# ---------------- Helper: convert image upload -> base64 ----------------
+# ---------------- Helpers ----------------
 def image_to_base64(img: Image.Image):
-    """
-    Converte PIL image in data:image/jpeg;base64,...
-    (usato dall'UI: caricamento immagine -> si popola la textbox base64)
-    """
     if img is None:
         return ""
-    buffer = io.BytesIO()
-    img.save(buffer, format="JPEG", quality=90)
-    b64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
-    return "data:image/jpeg;base64," + b64
+    buf = io.BytesIO()
+    img.save(buf, format="JPEG", quality=90)
+    return "data:image/jpeg;base64," + base64.b64encode(buf.getvalue()).decode("utf-8")
 
 def clear_box():
     return ""
 
-# ---------------- UI (Blocks) ----------------
-with gr.Blocks(title="NSFW Image Classifier (base64 single-input)"):
+# ---------------- UI ----------------
+with gr.Blocks(title="NSFW Image Classifier (API standard)") as demo:
     gr.HTML(f"""
     <div style="padding:12px; background:linear-gradient(135deg,#f8f9fa 0%,#e9ecef 100%); border-radius:8px;">
         <h2 style="color:{theme_color}; margin:0;">🎨 NSFW Image Classifier</h2>
-        <p style="margin:6px 0 0 0;">Carica un'immagine oppure incolla la base64. L'API accetta solo base64.</p>
+        <p style="margin:6px 0 0 0;">Carica un'immagine oppure incolla la base64. L'API espone solo <b>/api/predict</b>.</p>
     </div>
     """)
     with gr.Row():
         with gr.Column(scale=2):
-            image_input = gr.Image(label="📷 Carica immagine (verrà convertita in base64)", type="pil")
-            base64_input = gr.Textbox(label="📤 Base64 (API) — unico input", lines=6,
-                                     placeholder="Incolla qui la stringa base64 (data:image/..;base64,...)")
+            image_input = gr.Image(label="📷 Carica immagine (convertita in base64)", type="pil")
+            base64_input = gr.Textbox(label="📤 Base64 (API)", lines=6,
+                                      placeholder="Incolla qui la stringa base64...")
             with gr.Row():
-                analyze_btn = gr.Button("✨ Analizza (usa la base64 sopra)")
+                analyze_btn = gr.Button("✨ Analizza")
                 clear_btn = gr.Button("🔄 Pulisci")
         with gr.Column(scale=1):
             label_output = gr.Textbox(label="Classe predetta", interactive=False)
             result_display = gr.Label(label="Distribuzione probabilità", num_top_classes=len(labels))
 
-    # quando carichi immagine -> converto e popolo la textbox con la base64
+    # Carica immagine -> converte in base64 e riempie textbox
     image_input.change(fn=image_to_base64, inputs=image_input, outputs=base64_input)
 
-    # quando la base64 cambia -> chiamo predict (questo espone automaticamente l'endpoint API
-    # che accetta solo la textbox base64; gradio mappa l'endpoint in /run/predict in locale)
-    base64_input.change(fn=predict, inputs=base64_input, outputs=[label_output, result_display], api_name="predict")
-
-    # pulsante per analizzare manualmente (usa la base64 contenuta nella textbox)
-    analyze_btn.click(fn=predict, inputs=base64_input, outputs=[label_output, result_display])
+    # Analizza manualmente (API unica)
+    analyze_btn.click(fn=predict, inputs=base64_input, outputs=[label_output, result_display], api_name="predict")
 
     clear_btn.click(fn=clear_box, inputs=None, outputs=base64_input)
 
-# ---------------- LAUNCH ----------------
+# ---------------- Launch ----------------
 if __name__ == "__main__":
-    # show_api=True per vedere il link "View API" nella UI (opzionale)
-    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=True)
-
+    demo.launch(server_name="0.0.0.0", server_port=7860)