Delete app_final.py

app_final.py

@@ -1,86 +0,0 @@
-import gradio as gr
-import torch, torch.nn as nn, warnings
-from torchvision import transforms
-from transformers import EfficientNetModel
-from PIL import Image
-import numpy as np
-import os
-warnings.filterwarnings("ignore")
-
-class FFTBranch(nn.Module):
-    def __init__(self, out_dim=512):
-        super().__init__()
-        self.cnn = nn.Sequential(
-            nn.Conv2d(1,32,3,padding=1),nn.BatchNorm2d(32),nn.GELU(),nn.MaxPool2d(2),
-            nn.Conv2d(32,64,3,padding=1),nn.BatchNorm2d(64),nn.GELU(),nn.MaxPool2d(2),
-            nn.Conv2d(64,128,3,padding=1),nn.BatchNorm2d(128),nn.GELU(),
-            nn.AdaptiveAvgPool2d((4,4)),
-        )
-        self.proj = nn.Sequential(nn.Linear(128*4*4,out_dim),nn.GELU(),nn.Dropout(0.3))
-    def forward(self, x):
-        g=x.mean(dim=1,keepdim=True)
-        fft=torch.fft.fftshift(torch.fft.fft2(g))
-        mag=torch.log(torch.abs(fft)+1e-8)
-        mn=mag.flatten(2).min(2)[0].unsqueeze(-1).unsqueeze(-1)
-        mx=mag.flatten(2).max(2)[0].unsqueeze(-1).unsqueeze(-1)
-        mag=(mag-mn)/(mx-mn+1e-8)
-        return self.proj(self.cnn(mag).flatten(1))
-
-class CNNFFTDetector(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.cnn=EfficientNetModel.from_pretrained("google/efficientnet-b0")
-        params=list(self.cnn.parameters())
-        for i,p in enumerate(params):
-            p.requires_grad=(i>=int(len(params)*0.6))
-        self.cnn_proj=nn.Sequential(nn.Linear(1280,512),nn.GELU(),nn.Dropout(0.3))
-        self.fft=FFTBranch(out_dim=512)
-        self.classifier=nn.Sequential(
-            nn.Linear(1024,256),nn.GELU(),nn.Dropout(0.4),
-            nn.Linear(256,64),nn.GELU(),nn.Linear(64,1))
-    def forward(self, x):
-        c=self.cnn_proj(self.cnn(x).pooler_output)
-        f=self.fft(x)
-        return self.classifier(torch.cat([c,f],dim=1))
-
-print("Loading model...")
-device = torch.device("cpu")
-model  = CNNFFTDetector().to(device)
-ckpt   = torch.load("best.pth", map_location="cpu")
-model.load_state_dict(ckpt["model_state"])
-model.eval()
-print(f"Model ready — {ckpt[\'best_val_acc\']*100:.2f}%")
-
-tf = transforms.Compose([
-    transforms.Resize((224,224)),
-    transforms.ToTensor(),
-    transforms.Normalize([0.5]*3,[0.5]*3),
-])
-
-def predict(image):
-    if image is None:
-        return {"AI Generated": 0.0, "Real": 1.0}, "Please upload an image"
-    if isinstance(image, np.ndarray):
-        image = Image.fromarray(image)
-    tensor = tf(image).unsqueeze(0).to(device)
-    with torch.no_grad():
-        score = torch.sigmoid(model(tensor)).item()
-    fake_pct = round(score * 100, 1)
-    real_pct = round((1 - score) * 100, 1)
-    verdict = f"{\'🔴\' if score >= 0.5 else \'🟢\'} {\'AI Generated\' if score >= 0.5 else \'Real Image\'} | AI: {fake_pct}% | Real: {real_pct}%"
-    return {"AI Generated": float(score), "Real": float(1-score)}, verdict
-
-with gr.Blocks(theme=gr.themes.Soft(), title="LunaNet") as demo:
-    gr.Markdown("# 🌙 LunaNet — AI Image & Deepfake Detector\n**CNN (EfficientNetB0) + FFT** · 91.47% accuracy")
-    with gr.Row():
-        with gr.Column():
-            img_input = gr.Image(type="pil", label="Upload Image")
-            btn = gr.Button("✦ Analyse", variant="primary", size="lg")
-        with gr.Column():
-            label_out = gr.Label(num_top_classes=2, label="Detection Result")
-            md_out    = gr.Markdown()
-    btn.click(fn=predict, inputs=img_input, outputs=[label_out, md_out])
-    img_input.upload(fn=predict, inputs=img_input, outputs=[label_out, md_out])
-    gr.Markdown("---\n**Training data:** CIFAKE · 140k Faces · OpenForensics · Celeb-DF v2 · 46,010 images")
-
-demo.launch()