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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| #from transformers import pipeline | |
| from huggingface_hub.inference._providers import PROVIDER_OR_POLICY_T | |
| 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 = pipeline("text-generation", model="nosadaniel/llama3-1-8b-tuned") | |
| #client = InferenceClient(token=hf_token.token, model="nosadaniel/llama3-1-8b-tuned") | |
| model="meta-llama/Meta-Llama-3.1-8B-Instruct-LoRa:phishing-email-adJu" | |
| base_url="https://api.tokenfactory.nebius.com/v1/" | |
| api_key="v1.CmQKHHN0YXRpY2tleS1lMDBkMXh2ZDdheDAwNXhxMGgSIXNlcnZpY2VhY2NvdW50LWUwMGp0eHNrM3pubjdyYXQ0azIMCPHv7MgGEJ_k6PEBOgwI8PKElAcQwO2YywNAAloDZTAw.AAAAAAAAAAH-boLssQhDYJht_li9Ql7MN1rSmj_8DXmYlZ13NhdavV0NYylvY_HkVQrALXt2z9Pm5_aQn-tt--Mbc1W8G78E" | |
| client = InferenceClient( base_url=base_url, api_key=api_key, provider=PROVIDER_OR_POLICY_T) | |
| messages = [{"role": "system", "content": system_message}] | |
| messages.extend(history) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| for message in client.chat_completion( | |
| model = model, | |
| messages = 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 | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| system_prompt = """ | |
| # Parameters for email analysis | |
| PARAMETER temperature 0.1 | |
| PARAMETER top_p 0.9 | |
| PARAMETER top_k 40 | |
| PARAMETER repeat_penalty 1.1 | |
| PARAMETER num_ctx 4096 | |
| # Enhanced system prompt for email phishing detection | |
| SYSTEM | |
| You are an advanced AI security analyst specialized in email threat detection. Analyze the provided email data and determine if it constitutes a phishing attempt. | |
| Respond with exactly this JSON structure filled with real values (no backticks, no extra text): | |
| "" | |
| { | |
| "is_phishing": true or false, | |
| "confidence_score": a float between 0.0 and 1.0, | |
| "threat_type": "type of phishing attack", | |
| "risk_level": "a number from 0 to 5", | |
| "indicators": [ | |
| { | |
| "category": "which part of the email is suspicious", | |
| "finding": "concise finding", | |
| "severity": "a number from 0 to 5", | |
| "explanation": "short explanation referencing the email data" | |
| } | |
| ], | |
| "mitigation_recommendations": { | |
| "immediate_actions": ["short actionable steps"], | |
| "preventive_measures": ["short preventive steps"], | |
| "reporting_guidance": "who/how to report if applicable" | |
| }, | |
| "analysis_summary": "1-3 sentence summary of the assessment" | |
| } | |
| "" | |
| Only output the JSON object. | |
| # Fallback model with enhanced prompting | |
| # Base: Meta-Llama-3.1-8B-Instruct | |
| """ | |
| chatbot = gr.ChatInterface( | |
| respond, | |
| type="messages", | |
| additional_inputs=[ | |
| gr.Textbox(value=system_prompt, 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)", | |
| ), | |
| ], | |
| ) | |
| with gr.Blocks() as demo: | |
| # with gr.Sidebar(): | |
| # gr.LoginButton() | |
| chatbot.render() | |
| if __name__ == "__main__": | |
| demo.launch() |