Completed most of the nsfw classifyer

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/NSFW-Checker.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.13" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

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+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.13" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/NSFW-Checker.iml" filepath="$PROJECT_DIR$/.idea/NSFW-Checker.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="" vcs="Git" />
+  </component>
+</project>

app.py

@@ -1,70 +1,60 @@
+import os
+from json import dumps
 import gradio as gr
-from huggingface_hub import InferenceClient
+from huggingface_hub import login
+from transformers import pipeline
 
+from classify_utils import classify_image_if_nsfw, get_nsfw_classifier, get_normal_classifier
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
+global classifier
 
-    messages = [{"role": "system", "content": system_message}]
+def init_login():
+    try:
+        login(os.environ.get('HF_TOKEN'))
+        print("logged in successfully")
+    except Exception as e:
+        print("issue logging into hugging face:", e)
 
-    messages.extend(history)
 
-    messages.append({"role": "user", "content": message})
+def init_classifier():
+    classifier = pipeline("image-classification", model="Falconsai/nsfw_image_detection")
 
-    response = ""
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
 
-        response += token
-        yield response
+def respond(
+    message,
+    history: list[dict[str, str]],
+):
+    json_response = classify_image_if_nsfw(classifier, message)
+    print(dumps(json_response, indent=4))
+    normal_classifier = get_normal_classifier(json_response)
+    nsfw_classifier = get_nsfw_classifier(json_response)
+    # Fixed: Use 'and' instead of '&&', and improved logic flow
+    if nsfw_classifier is not None and nsfw_classifier['score'] > 0.75:
+        return {"text": "Very Explicit"}
+    elif nsfw_classifier is not None and nsfw_classifier['score'] > 0.5:
+        return {"text": "Somewhat Explicit"}
+    elif normal_classifier is not None and normal_classifier['score'] < 0.8:
+        return {"text": "Somewhat Normal"}
+    else:
+        return {"text": "Normal"}
+
 
 
 """
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+Functionality of nsfw detector
 """
+init_login()
+init_classifier()
+
 chatbot = gr.ChatInterface(
     respond,
     type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+    additional_inputs=[]
 )
 
 with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
     chatbot.render()
 
-
 if __name__ == "__main__":
     demo.launch()

classify_utils.py

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+import re
+import requests
+import base64
+from PIL import Image
+from io import BytesIO
+from transformers import ImageClassificationPipeline
+
+
+def get_normal_classifier(items: list[object])->object | None:
+    normal_classifier = next((item for item in items if item["label"] == "normal"), None)
+    return normal_classifier
+
+def get_nsfw_classifier(items: list[object])->object | None:
+    nsfw_classifier = next((item for item in items if item["label"] == "nsfw"), None)
+    return nsfw_classifier
+
+
+def classify_image_if_nsfw(classifier: ImageClassificationPipeline, image_url: str):
+    try:
+        # Check if it's a base64 data URL
+        if image_url.startswith('data:image'):
+            print("Processing base64 data URL")
+
+            # Extract the base64 data from the data URL
+            match = re.match(r'data:image/(?P<ext>\w+);base64,(?P<data>.*)', image_url)
+            if not match:
+                raise ValueError("Invalid base64 data URL format")
+
+            base64_data = match.group('data')
+            image_format = match.group('ext')
+
+            # Decode the base64 data
+            image_data = base64.b64decode(base64_data)
+
+            # Open the image from decoded data
+            img = Image.open(BytesIO(image_data))
+
+        else:
+            # It's a regular URL - download the image
+            print("Processing regular URL")
+            response = requests.get(image_url)
+            response.raise_for_status()
+
+            # Open and process the image
+            img = Image.open(BytesIO(response.content))
+
+        print("Image size:", img.size)
+        print("Image format:", img.format)
+        print("Image mode:", img.mode)
+
+        # Ensure image is in RGB mode (required by most models)
+        if img.mode != 'RGB':
+            img = img.convert('RGB')
+
+        # Classify the image
+        classifier_response = classifier(img)
+        print("Classifier Response:", classifier_response)
+        normal_classifier = classifier_response
+        return classifier_response
+
+    except Exception as e:
+        print(f"Error processing image: {e}")
+        raise

requirements.txt

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+gradio~=5.49.1
+gradio[oauth]
+requests~=2.32.4
+huggingface-hub~=0.36.0
+pillow~=11.3.0
+transformers~=4.53.2
+base64