Update app.py

app.py

@@ -1,115 +1,46 @@
 import gradio as gr
-import torch
-import torch.nn as nn
-from torchvision import transforms
-from torchvision.models import resnet18
-from PIL import Image
 import base64
-import io
-
-# ---------------- CONFIG ----------------
-labels = ["Drawings", "Hentai", "Neutral", "Porn", "Sexy"]
-theme_color = "#6C5B7B"
-
-# ---------------- MODEL ----------------
-class Classifier(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.cnn_layers = resnet18(weights=None)
-        self.fc_layers = nn.Sequential(
-            nn.Linear(1000, 512),
-            nn.Dropout(0.3),
-            nn.Linear(512, 128),
-            nn.ReLU(),
-            nn.Linear(128, len(labels))
-        )
-
-    def forward(self, x):
-        x = self.cnn_layers(x)
-        x = self.fc_layers(x)
-        return x
-
-preprocess = transforms.Compose([
-    transforms.Resize((224,224)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.485,0.456,0.406],[0.229,0.224,0.225])
-])
-
-model = Classifier()
-model.load_state_dict(torch.load("classify_nsfw_v3.0.pth", map_location="cpu"))
-model.eval()
+from io import BytesIO
+from PIL import Image
 
-# ---------------- FUNZIONI ----------------
-def predict(base64_input: str):
-    """
-    Unico input: stringa base64 (da API o da UI).
-    """
+# Funzione di analisi immagine base64
+def analyze_base64(b64_string: str):
     try:
-        if base64_input.startswith("data:image"):
-            base64_input = base64_input.split(",", 1)[1]
-
-        img_bytes = base64.b64decode(base64_input)
-        img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
-
-        img_tensor = preprocess(img).unsqueeze(0)
-        with torch.no_grad():
-            logits = model(img_tensor)
-            probs = torch.nn.functional.softmax(logits[0], dim=0)
-
-        probs_dict = {labels[i]: float(probs[i]) for i in range(len(labels))}
-        max_label = max(probs_dict, key=probs_dict.get)
-        return max_label, probs_dict
-
+        # Decodifica
+        if b64_string.startswith("data:image"):
+            b64_string = b64_string.split(",")[1]
+
+        image_data = base64.b64decode(b64_string)
+        img = Image.open(BytesIO(image_data))
+
+        # Qui metti il tuo modello / logica di analisi
+        # Per esempio: restituisco dimensioni e formato
+        result = {
+            "width": img.width,
+            "height": img.height,
+            "format": img.format
+        }
+        return f"Analisi completata: {result}"
     except Exception as e:
-        return f"Error: {str(e)}", {}
+        return f"Errore: {str(e)}"
 
-def image_to_base64(img: Image.Image):
-    """
-    Converte immagine caricata in base64 e la ritorna
-    così finisce nella Textbox e poi viene analizzata.
-    """
-    buffered = io.BytesIO()
-    img.save(buffered, format="JPEG")
-    img_b64 = base64.b64encode(buffered.getvalue()).decode()
-    return "data:image/jpeg;base64," + img_b64
+# Se carichi da web → converto subito in base64
+def file_to_base64(img: Image.Image):
+    buffered = BytesIO()
+    img.save(buffered, format="PNG")
+    return "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode()
 
-def clear_all():
-    return ""
-
-# ---------------- INTERFACCIA ----------------
-with gr.Blocks(title="NSFW Image Classifier") as demo:
-
-    gr.HTML(f"""
-    <div style="padding:10px; background:linear-gradient(135deg,#f8f9fa 0%,#e9ecef 100%); border-radius:10px;">
-        <h2 style="color:{theme_color};">🎨 NSFW Image Classifier</h2>
-        <p>Carica un'immagine o incolla la stringa base64.<br>
-        L'API espone <code>/run/predict</code> e accetta <b>solo base64</b>.</p>
-    </div>
-    """)
+with gr.Blocks() as demo:
+    gr.Markdown("## Analisi Immagini via Base64")
 
     with gr.Row():
-        with gr.Column(scale=2):
-            img_input = gr.Image(label="📷 Carica immagine", type="pil")
-            base64_input = gr.Textbox(
-                label="📤 Base64 dell'immagine (API)",
-                lines=6,
-                placeholder="Incolla qui la stringa base64..."
-            )
-            with gr.Row():
-                submit_btn = gr.Button("✨ Analizza", variant="primary")
-                clear_btn = gr.Button("🔄 Pulisci", variant="secondary")
-
-        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))
+        img_input = gr.Image(type="pil", label="Carica immagine (verrà convertita in base64)")
+        b64_input = gr.Textbox(label="📤 Base64 dell'immagine (API)", lines=6)
 
-    # Se carico immagine → la converto in base64 → la inserisco nella Textbox
-    img_input.change(fn=image_to_base64, inputs=img_input, outputs=base64_input)
+    output = gr.Textbox(label="Risultato analisi")
 
-    # Submit → usa sempre base64 come input
-    submit_btn.click(fn=predict, inputs=base64_input, outputs=[label_output, result_display])
-    clear_btn.click(fn=clear_all, inputs=None, outputs=base64_input)
+    img_input.change(fn=file_to_base64, inputs=img_input, outputs=b64_input)
+    b64_input.submit(fn=analyze_base64, inputs=b64_input, outputs=output)
 
-# ---------------- LAUNCH ----------------
-if __name__ == "__main__":
-    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=True)
+# Avvio server
+demo.launch(server_name="0.0.0.0", server_port=7860)