Xhaheen commited on
Commit
85a09fb
·
verified ·
1 Parent(s): 43f5dc5

Update app.py

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Files changed (1) hide show
  1. app.py +12 -10
app.py CHANGED
@@ -19,7 +19,7 @@ client = OpenAI(
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  api_key=YOUR__API_KEY,
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  )
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- models = [
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  "google/gemini-2.5-flash-lite",
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  "google/gemini-2.0-flash-lite-001",
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  "google/gemma-3-27b-it",
@@ -129,12 +129,14 @@ def assess_text_harmfulness(input_text, fallback_models):
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  }
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  return json.dumps(json_data, indent=4), format_json_output(json_data)
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- models_to_try = fallback_models
 
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- for try_model in models_to_try:
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  try:
 
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  resp = client.chat.completions.create(
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- model=try_model,
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  messages=[
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  {
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  "role": "user",
@@ -395,14 +397,14 @@ theme = gr.themes.Glass(
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  with gr.Blocks(theme=theme, css=light_blue_glass_css, title="Falconz Unified App") as demo:
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-
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  gr.Markdown(""" # 🔐 Falconz - RedTeamers
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  ### 🛡️ Unified AI Security for Multi-Model & Agentic Systems
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- Falconz is an MCP-powered Gradio platform that safeguards LLM and agentic applications through real-time jailbreak and prompt-injection detection across OpenAI, Gemini, Mistral, Phi, and more.
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- It includes an inbuilt library of the latest Top 10 jailbreak templates (Inspired by O.S.W.A.P) that users can customize, modify, and deploy for controlled testing and red-teaming workflows.
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- Falconz supports both prompt template modification and threat detection—letting users experiment safely while continuously monitoring model behavior.
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- A lightweight safeguard model provides fast, on-device-friendly risk screening for quick evaluation cycles.
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  Plug-and-play with MCP to secure your AI stack and access live analytics in a single, streamlined interface.
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  """)
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@@ -412,7 +414,7 @@ with gr.Blocks(theme=theme, css=light_blue_glass_css, title="Falconz Unified App
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  with gr.Row():
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  with gr.Column(scale=50):
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  model_select = gr.Radio(choices=models, value=models[0], label="Select Model ")
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-
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  chatbot = gr.Chatbot(label="Chat History", height=300, type='messages')
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  user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=5)
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  api_key=YOUR__API_KEY,
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  )
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+ models = ["anthropic/claude-sonnet-4.5",
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  "google/gemini-2.5-flash-lite",
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  "google/gemini-2.0-flash-lite-001",
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  "google/gemma-3-27b-it",
 
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  }
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  return json.dumps(json_data, indent=4), format_json_output(json_data)
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+ # models_to_try = fallback_models
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+ models_to_try = ["anthropic/claude-sonnet-4.5", "anthropic/claude-sonnet-4", "anthropic/claude-haiku-4.5", "anthropic/claude-opus-4.5", "anthropic/claude-3.5-haiku"]
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+ for Detective_model in models_to_try:
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  try:
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+ print(f"model used as detective is {Detective_model}")
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  resp = client.chat.completions.create(
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+ model=Detective_model,
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  messages=[
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  {
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  "role": "user",
 
397
 
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  with gr.Blocks(theme=theme, css=light_blue_glass_css, title="Falconz Unified App") as demo:
399
 
400
+
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  gr.Markdown(""" # 🔐 Falconz - RedTeamers
402
 
403
  ### 🛡️ Unified AI Security for Multi-Model & Agentic Systems
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+ Falconz is an MCP-powered Gradio platform that safeguards LLM and agentic applications through real-time jailbreak and prompt-injection detection across OpenAI, Gemini, Mistral, Phi, and more.
405
+ It includes an inbuilt library of the latest Top 10 jailbreak templates (Inspired by O.S.W.A.P) that users can customize, modify, and deploy for controlled testing and red-teaming workflows.
406
+ Falconz supports both prompt template modification and threat detection—letting users experiment safely while continuously monitoring model behavior.
407
+ A lightweight safeguard model provides fast, on-device-friendly risk screening for quick evaluation cycles.
408
  Plug-and-play with MCP to secure your AI stack and access live analytics in a single, streamlined interface.
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  """)
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  with gr.Row():
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  with gr.Column(scale=50):
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  model_select = gr.Radio(choices=models, value=models[0], label="Select Model ")
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+
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  chatbot = gr.Chatbot(label="Chat History", height=300, type='messages')
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  user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...", lines=5)
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