Update app.py

app.py

@@ -4,7 +4,30 @@ from huggingface_hub import InferenceClient
 """
 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("HuggingFaceH4/zephyr-7b-beta")
+client = InferenceClient("bert-base-uncased")  # Replace with a relevant model for classification or detection
+
+def detect_passwords(text, threshold=0.9):
+    """
+    Simulates BERT model usage to detect possible passwords.
+    :param text: Input text containing potential passwords.
+    :param threshold: Confidence score above which a pattern is flagged.
+    :return: Flagged patterns and their confidence scores.
+    """
+    # Simulated model output (replace with actual BERT inference if supported)
+    tokens = text.split()  # Tokenize the input text (basic example)
+    flagged_items = []
+    for token in tokens:
+        # Simulate a condition where tokens matching a simple regex-like pattern are flagged
+        if len(token) > 6 and any(c.isdigit() for c in token) and any(c.isalpha() for c in token):
+            # Simulate confidence score
+            confidence_score = 0.95  # Example confidence for demo purposes
+            if confidence_score > threshold:
+                flagged_items.append((token, confidence_score))
+    
+    if not flagged_items:
+        return "No passwords detected."
+    else:
+        return f"Potential passwords detected: {flagged_items}"
 
 
 def respond(
@@ -15,6 +38,9 @@ def respond(
     temperature,
     top_p,
 ):
+    """
+    Processes the user's message and history, integrates the password sniffer functionality.
+    """
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -23,21 +49,12 @@ def respond(
         if val[1]:
             messages.append({"role": "assistant", "content": val[1]})
 
-    messages.append({"role": "user", "content": message})
-
-    response = ""
+    # Simulate BERT-driven password detection for the user's message
+    detected_passwords = detect_passwords(message)
+    response = detected_passwords
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
+    return response  # Outputs directly for demo purposes
 
-        response += token
-        yield response
 
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
@@ -45,7 +62,7 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a password detection 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(
@@ -60,4 +77,4 @@ demo = gr.ChatInterface(
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()