Update app.py

app.py

@@ -1,15 +1,22 @@
 import gradio as gr
 import torch
 from transformers import AutoFeatureExtractor, AutoModelForImageClassification, pipeline
-import os 
-from numpy import exp
-import pandas as pd
+import os
+import zipfile
+import shutil
+import matplotlib.pyplot as plt
+from sklearn.metrics import accuracy_score, roc_auc_score, confusion_matrix, classification_report, roc_curve, auc
+from tqdm import tqdm
 from PIL import Image
-import urllib.request 
 import uuid
-uid = uuid.uuid4()
+import tempfile
+import pandas as pd
+from numpy import exp
+import numpy as np
+from sklearn.metrics import ConfusionMatrixDisplay
+import urllib.request
 
-# Reordered models as requested
+# Define models
 models = [
     "umm-maybe/AI-image-detector",
     "Organika/sdxl-detector",
@@ -21,11 +28,78 @@ pipe1 = pipeline("image-classification", f"{models[1]}")
 pipe2 = pipeline("image-classification", f"{models[2]}")
 
 fin_sum = []
+uid = uuid.uuid4()
 
+# Softmax function
 def softmax(vector):
     e = exp(vector - vector.max())  # for numerical stability
     return e / e.sum()
 
+# Function to extract images from zip
+def extract_zip(zip_file):
+    temp_dir = tempfile.mkdtemp()  # Temporary directory
+    with zipfile.ZipFile(zip_file, 'r') as z:
+        z.extractall(temp_dir)
+    return temp_dir
+
+# Function to classify images in a folder
+def classify_images(image_dir, model_pipeline):
+    images = []
+    labels = []
+    preds = []
+    for folder_name, ground_truth_label in [('real', 1), ('ai', 0)]:
+        folder_path = os.path.join(image_dir, folder_name)
+        if not os.path.exists(folder_path):
+            continue
+        for img_name in os.listdir(folder_path):
+            img_path = os.path.join(folder_path, img_name)
+            try:
+                img = Image.open(img_path).convert("RGB")
+                pred = model_pipeline(img)
+                pred_label = np.argmax([x['score'] for x in pred])
+                preds.append(pred_label)
+                labels.append(ground_truth_label)
+                images.append(img_name)
+            except Exception as e:
+                print(f"Error processing image {img_name}: {e}")
+    return labels, preds, images
+
+# Function to generate evaluation metrics
+def evaluate_model(labels, preds):
+    cm = confusion_matrix(labels, preds)
+    accuracy = accuracy_score(labels, preds)
+    roc_score = roc_auc_score(labels, preds)
+    report = classification_report(labels, preds)
+    fpr, tpr, _ = roc_curve(labels, preds)
+    roc_auc = auc(fpr, tpr)
+
+    fig, ax = plt.subplots()
+    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=["AI", "Real"])
+    disp.plot(cmap=plt.cm.Blues, ax=ax)
+    plt.close(fig)
+
+    fig_roc, ax_roc = plt.subplots()
+    ax_roc.plot(fpr, tpr, color='blue', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
+    ax_roc.plot([0, 1], [0, 1], color='gray', linestyle='--')
+    ax_roc.set_xlim([0.0, 1.0])
+    ax_roc.set_ylim([0.0, 1.05])
+    ax_roc.set_xlabel('False Positive Rate')
+    ax_roc.set_ylabel('True Positive Rate')
+    ax_roc.set_title('Receiver Operating Characteristic (ROC) Curve')
+    ax_roc.legend(loc="lower right")
+    plt.close(fig_roc)
+
+    return accuracy, roc_score, report, fig, fig_roc
+
+# Gradio function for batch image processing
+def process_zip(zip_file):
+    extracted_dir = extract_zip(zip_file.name)
+    labels, preds, images = classify_images(extracted_dir, pipe0)  # You can switch to pipe1 or pipe2
+    accuracy, roc_score, report, cm_fig, roc_fig = evaluate_model(labels, preds)
+    shutil.rmtree(extracted_dir)  # Clean up extracted files
+    return accuracy, roc_score, report, cm_fig, roc_fig
+
+# Single image classification functions
 def image_classifier0(image):
     labels = ["AI", "Real"]
     outputs = pipe0(image)
@@ -70,8 +144,8 @@ def aiornot0(image):
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
     Probabilities:<br>
-    Real: {float(px[1][0])}<br>
-    AI: {float(px[0][0])}"""
+    Real: {float(px[1][0]):.4f}<br>
+    AI: {float(px[0][0]):.4f}"""
     
     results = {
         "Real": float(px[1][0]),
@@ -97,8 +171,8 @@ def aiornot1(image):
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
     Probabilities:<br>
-    Real: {float(px[1][0])}<br>
-    AI: {float(px[0][0])}"""
+    Real: {float(px[1][0]):.4f}<br>
+    AI: {float(px[0][0]):.4f}"""
     
     results = {
         "Real": float(px[1][0]),
@@ -124,8 +198,8 @@ def aiornot2(image):
     html_out = f"""
     <h1>This image is likely: {label}</h1><br><h3>
     Probabilities:<br>
-    Real: {float(px[1][0])}<br>
-    AI: {float(px[0][0])}"""
+    Real: {float(px[1][0]):.4f}<br>
+    AI: {float(px[0][0]):.4f}"""
     
     results = {
         "Real": float(px[1][0]),
@@ -149,8 +223,8 @@ def tot_prob():
         fin_out = sum([result["Real"] for result in fin_sum]) / len(fin_sum)
         fin_sub = 1 - fin_out
         out = {
-            "Real": f"{fin_out}",
-            "AI": f"{fin_sub}"
+            "Real": f"{fin_out:.4f}",
+            "AI": f"{fin_sub:.4f}"
         }
         return out
     except Exception as e:
@@ -167,50 +241,56 @@ def upd(image):
     out = Image.open(f"{rand_im}-vid_tmp_proc.png")
     return out
 
+# Set up Gradio app
 with gr.Blocks() as app:
-    gr.Markdown("""<center><h1>AI Image Detector<br><h4>(Test Demo - accuracy varies by model)""")
-    with gr.Column():
-        inp = gr.Image(type='pil')
-        in_url = gr.Textbox(label="Image URL")
-        with gr.Row():
-            load_btn = gr.Button("Load URL")
-            btn = gr.Button("Detect AI")
-        mes = gr.HTML("""""")
-
-    with gr.Group():
-        with gr.Row():
-            fin = gr.Label(label="Final Probability", visible=False)
-        with gr.Row():
-            # Updated model names
-            with gr.Box():
-                lab0 = gr.HTML(f"""<b>Testing on Original Model: <a href='https://huggingface.co/{models[0]}'>{models[0]}</a></b>""")
-                nun0 = gr.HTML("""""")
-            with gr.Box():
-                lab1 = gr.HTML(f"""<b>Testing on SDXL Fine Tuned Model: <a href='https://huggingface.co/{models[1]}'>{models[1]}</a></b>""")
-                nun1 = gr.HTML("""""")
-            with gr.Box():
-                lab2 = gr.HTML(f"""<b>Testing on SDXL and Flux Fine Tuned Model: <a href='https://huggingface.co/{models[2]}'>{models[2]}</a></b>""")
-                nun2 = gr.HTML("""""")
-        with gr.Row():
-            with gr.Box():
-                n_out0 = gr.Label(label="Output")
-                outp0 = gr.HTML("""""")
-            with gr.Box():
-                n_out1 = gr.Label(label="Output")
-                outp1 = gr.HTML("""""")
-            with gr.Box():
-                n_out2 = gr.Label(label="Output")
-                outp2 = gr.HTML("""""")
-
-    btn.click(fin_clear, None, fin, show_progress=False)
-    load_btn.click(load_url, in_url, [inp, mes])
-
-    btn.click(aiornot0, [inp], [outp0, n_out0]).then(tot_prob, None, fin, show_progress=False)
-    btn.click(aiornot1, [inp], [outp1, n_out1]).then(tot_prob, None, fin, show_progress=False)
-    btn.click(aiornot2, [inp], [outp2, n_out2]).then(tot_prob, None, fin, show_progress=False)
-
-    btn.click(image_classifier0, [inp], [n_out0]).then(tot_prob, None, fin, show_progress=False)
-    btn.click(image_classifier1, [inp], [n_out1]).then(tot_prob, None, fin, show_progress=False)
-    btn.click(image_classifier2, [inp], [n_out2]).then(tot_prob, None, fin, show_progress=False)
-
-app.launch(show_api=False, max_threads=24)
+    gr.Markdown("""<center><h1>AI Image Detector<br><h4>(Test Demo - accuracy varies by model)</h4></h1></center>""")
+
+    with gr.Tabs():
+        # Tab for single image detection
+        with gr.Tab("Single Image Detection"):
+            with gr.Column():
+                inp = gr.Image(type='pil')
+                in_url = gr.Textbox(label="Image URL")
+                with gr.Row():
+                    load_btn = gr.Button("Load URL")
+                    btn = gr.Button("Detect AI")
+                mes = gr.HTML("""""")
+
+            with gr.Group():
+                with gr.Row():
+                    fin = gr.Label(label="Final Probability")
+                with gr.Row():
+                    for i, model in enumerate(models):
+                        with gr.Box():
+                            gr.HTML(f"""<b>Testing on Model {i}: <a href='https://huggingface.co/{model}'>{model}</a></b>""")
+                            globals()[f'outp{i}'] = gr.HTML("""""")
+                            globals()[f'n_out{i}'] = gr.Label(label="Output")
+
+            btn.click(fin_clear, None, fin, show_progress=False)
+            load_btn.click(load_url, in_url, [inp, mes])
+
+            btn.click(aiornot0, [inp], [outp0, n_out0]).then(
+                aiornot1, [inp], [outp1, n_out1]).then(
+                aiornot2, [inp], [outp2, n_out2]).then(
+                tot_prob, None, fin, show_progress=False)
+
+            btn.click(image_classifier0, [inp], [n_out0]).then(
+                image_classifier1, [inp], [n_out1]).then(
+                image_classifier2, [inp], [n_out2]).then(
+                tot_prob, None, fin, show_progress=False)
+
+        # Tab for batch processing
+        with gr.Tab("Batch Image Processing"):
+            zip_file = gr.File(label="Upload Zip (two folders: real, ai)")
+            output_acc = gr.Label(label="Accuracy")
+            output_roc = gr.Label(label="ROC Score")
+            output_report = gr.Textbox(label="Classification Report", lines=10)
+            output_cm = gr.Plot(label="Confusion Matrix")
+            output_roc_plot = gr.Plot(label="ROC Curve")
+
+            batch_btn = gr.Button("Process Batch")
+
+    # Connect batch processing
+    batch_btn.click(process_zip, zip_file, [output_acc, output_roc, output_report, output_cm, output_roc_plot])
+
+app.launch(show_api=False, max_threads=24)