Upload Image_Origin_Classification.py

Image_Origin_Classification.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[29]:
+
+
+from PIL import Image
+import torchvision.transforms.functional as TF
+from torchvision import transforms
+import torchvision.models as models
+from torchvision.datasets import ImageFolder
+from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, random_split
+import torchvision
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+pd.DataFrame.iteritems = pd.DataFrame.items
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import gradio as gr
+
+
+# In[11]:
+
+
+classes = ['Fake_Copilot', 'Fake_DreamStudio', 'Fake_Gemini', 'Real']
+
+
+# In[16]:
+
+
+d_path = '/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/dense.pth'
+g_path = '/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/google.pth'
+r_path = '/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/resnet.pth'
+v_path = '/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/vgg13.pth'
+
+
+# In[17]:
+
+
+dense_net = models.densenet161()
+dense_net.classifier = nn.Linear(2208, len(classes), bias = True)
+dense_net.load_state_dict(torch.load(d_path))
+
+
+# In[18]:
+
+
+googlenet = models.googlenet()
+googlenet.fc = nn.Linear(1024, len(classes), bias = True)
+googlenet.load_state_dict(torch.load(g_path))
+
+
+# In[19]:
+
+
+vgg13 = models.vgg13()
+vgg13.classifier[6] = nn.Linear(4096, len(classes), bias = True)
+vgg13.load_state_dict(torch.load(v_path))
+
+
+# In[20]:
+
+
+res_net = models.resnet101()
+res_net.fc = nn.Linear(2048, len(classes), bias = True)
+res_net.load_state_dict(torch.load(r_path))
+
+
+# In[24]:
+
+
+transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+
+
+# In[27]:
+
+
+def one_prediction(img):
+    preds = {classname: 0 for classname in classes}
+    #img = Image.open(path).convert('RGB')
+    img = transform(img)
+    img.unsqueeze_(0)
+    models = [dense_net, googlenet, vgg13, res_net]
+    #dense_net.eval()
+    with torch.no_grad():
+        for model in models:
+            model.eval()
+            output = model(img)
+            _, predicted = torch.max(output.data, 1)
+            preds[classes[predicted]] += 1
+    for classname, count in preds.items():
+        chance = float(count) / len(classes)
+        preds[classname] = chance
+    return preds
+
+
+# In[28]:
+
+
+#path = 'C:/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/One/24June Batch (80).png'
+#img = Image.open(path).convert('RGB')
+#one_prediction(img)
+
+
+# In[30]:
+
+
+title = "Real vs Fake Image Classification"
+description = "Test."
+article = "Test"
+examples = [['C:/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/One/24June Batch (80).png'],
+           ['C:/Users/robb4/OneDrive/Desktop/DATA SCIENCE MPS/Summer 24/Deep Learning/Final Project/One/antarctica_0231.png']]
+
+demo = gr.Interface(fn=one_prediction, 
+                    inputs=gr.Image(type="pil"),
+                    outputs=gr.Label(num_top_classes=4, label="Predictions"),
+                    examples=examples, 
+                    title=title,
+                    description=description,
+                    article=article)
+demo.launch(debug=False,
+            share=True)
+
+
+# In[ ]:
+
+
+
+