app.py

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(mean=[0.485,0.456,0.406],
                         std=[0.229,0.224,0.225])
])

model = Classifier()
model.load_state_dict(torch.load("classify_nsfw_v3.0.pth", map_location="cpu"))
model.eval()

# ---------------- FUNZIONE ----------------
def predict(image_input):
    """
    Supporta:
    - PIL Image (UI web)
    - stringa base64 (API)
    """
    try:
        if isinstance(image_input, str):
            if image_input.startswith("data:image"):
                image_input = image_input.split(",",1)[1]
            img_bytes = base64.b64decode(image_input)
            img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
        else:
            img = image_input.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

    except Exception as e:
        return f"Error: {str(e)}", {}

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 per analizzarla.</p>
    </div>
    """)

    with gr.Row():
        with gr.Column(scale=2):
            # Input UI
            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))

    # ---------------- Eventi UI ----------------
    submit_btn.click(
        fn=predict,
        inputs=[img_input],
        outputs=[label_output, result_display]
    )
    clear_btn.click(fn=clear_all, inputs=None, outputs=[img_input, base64_input])

    # ---------------- Pulsante invisibile per API base64 ----------------
    api_button = gr.Button(visible=False)
    api_button.click(
        fn=predict,
        inputs=[base64_input],
        outputs=[label_output, result_display],
        api_name="predict"  # espone /run/predict
    )

# ---------------- LAUNCH ----------------
if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, show_api=True)