Update app.py

app.py

@@ -1,63 +1,59 @@
 import gradio as gr
-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("Canstralian/RedTeamAI")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    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
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    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)",
-        ),
+from transformers import pipeline
+import subprocess
+
+# Load your Hugging Face model
+model_name = "username/RedTeamAI-new"  # Replace with your Hugging Face model path
+chatbot = pipeline("text-generation", model=model_name)
+
+def generate_response(prompt):
+    """Generate a response using the Hugging Face model."""
+    try:
+        response = chatbot(prompt, max_length=150, num_return_sequences=1)
+        return response[0]["generated_text"]
+    except Exception as e:
+        return f"Model Error: {str(e)}"
+
+def execute_bash(command):
+    """Execute a Bash command and return the output."""
+    try:
+        result = subprocess.run(command, shell=True, capture_output=True, text=True)
+        return result.stdout or result.stderr
+    except Exception as e:
+        return f"Execution Error: {str(e)}"
+
+def execute_python(script):
+    """Execute a Python script dynamically."""
+    try:
+        exec_globals = {}
+        exec(script, exec_globals)
+        return "Script executed successfully!"
+    except Exception as e:
+        return f"Python Execution Error: {str(e)}"
+
+def chatbot_interface(user_input, execution_mode, code=None):
+    """Main interface logic for guiding and executing scripts."""
+    if execution_mode == "Guide":
+        return generate_response(user_input)
+    elif execution_mode == "Execute Bash":
+        return execute_bash(user_input)
+    elif execution_mode == "Execute Python":
+        return execute_python(code)
+    else:
+        return "Invalid mode selected!"
+
+# Gradio Interface
+interface = gr.Interface(
+    fn=chatbot_interface,
+    inputs=[
+        gr.Textbox(label="Enter your query or script"), 
+        gr.Radio(["Guide", "Execute Bash", "Execute Python"], label="Mode"),
+        gr.Textbox(label="Python Script (if applicable)", lines=10, optional=True)
     ],
+    outputs="text",
+    title="RedTeamAI Script Assistant",
+    description="A GPT-powered chatbot to guide through and execute Bash and Python scripts."
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()