url feature added

app.py

@@ -1,43 +1,69 @@
-import gradio as gr 
+import gradio as gr
 import numpy as np
-import urllib
+import urllib.request
+from PIL import Image
+from io import BytesIO
 from tensorflow.keras.preprocessing import image
 from tensorflow.keras.models import load_model
 
-# Load model (skip optimizer state)
-model = load_model('my_model.h5', compile=False)
+# Load the model
+model = load_model("my_model.h5", compile=False)
 
-# Prediction function
-def classify_image(img):
-    # Resize for model
-    img = image.array_to_img(img).resize((224, 224))
+# Common prediction function
+def classify_pil_image(pil_img):
+    img = pil_img.resize((224, 224))
     img = image.img_to_array(img)
     img = np.expand_dims(img, axis=0)
     img = img / 255.0
-
     prediction = model.predict(img)[0]
-    result = {
-        'CART': float(prediction[0]),
-        'NSFW': float(prediction[1]),
-        'SFW': float(prediction[2])
+    return {
+        "CART": float(prediction[0]),
+        "NSFW": float(prediction[1]),
+        "SFW": float(prediction[2])
     }
-    return result
-
-# Example images
-examples = [f"example{i}.jpg" for i in range(1, 9)]
-
-# Gradio interface
-app = gr.Interface(
-    fn=classify_image,
-    inputs=gr.Image(
-        image_mode="RGB", 
-        label="📂 Drag and drop or click to upload an image"
-    ),
-    outputs=gr.Label(num_top_classes=3),
-    allow_flagging="never",
+
+# From file input (or example)
+def classify_uploaded_image(file):
+    try:
+        pil_img = Image.fromarray(file).convert("RGB")
+        return classify_pil_image(pil_img)
+    except Exception as e:
+        return {"error": f"Upload error: {str(e)}"}
+
+# From URL input
+def classify_from_url(url):
+    try:
+        response = urllib.request.urlopen(url)
+        img = Image.open(BytesIO(response.read())).convert("RGB")
+        return classify_pil_image(img)
+    except Exception as e:
+        return {"error": f"URL error: {str(e)}"}
+
+# Example images for file-based interface
+examples = [[f"example{i}.jpg"] for i in range(1, 9)]
+
+# Upload tab (classic layout with examples)
+upload_interface = gr.Interface(
+    fn=classify_uploaded_image,
+    inputs=gr.Image(type="numpy", label="Upload or drag an image"),
+    outputs=gr.Label(num_top_classes=3, label="Prediction"),
     examples=examples,
-    title="Simple NSFW/SFW/CART Classifier"
+    title="Simple NSFW/SFW/CART Classifier",
+    allow_flagging="never",
+    cache_examples=False
 )
 
-app.launch(server_name="0.0.0.0", server_port=7860)
+# URL tab (simple textbox interface)
+url_interface = gr.Interface(
+    fn=classify_from_url,
+    inputs=gr.Textbox(label="Paste Image URL"),
+    outputs=gr.Label(num_top_classes=3, label="Prediction"),
+    allow_flagging="never",
+    cache_examples=False
+)
 
+# Tabs wrapper to combine them
+gr.TabbedInterface(
+    [upload_interface, url_interface],
+    tab_names=["📤 Upload Image", "🌐 Image URL"]
+).launch()

requirements.txt

@@ -1,4 +1,4 @@
-tensorflow==2.18.1        # latest stable from TensorFlow GitHub :contentReference[oaicite:1]{index=1}
+tensorflow>=2.10        # latest stable from TensorFlow GitHub :contentReference[oaicite:1]{index=1}
 opencv-python-headless==4.11.0.86  # latest from PyPI :contentReference[oaicite:2]{index=2}
 gradio==5.34.0            # just released today on PyPI :contentReference[oaicite:3]{index=3}
 numpy>=2.0.2             # latest major release as of June 7, 2025 :contentReference[oaicite:4]{index=4}