Upload 7 files

README.md

@@ -1,8 +1,8 @@
 ---
-title: Finance Guide
-emoji: 📚
-colorFrom: indigo
-colorTo: blue
+title: Finance Chatbot With Guardrails
+emoji: 📉
+colorFrom: purple
+colorTo: red
 sdk: gradio
 sdk_version: 5.8.0
 app_file: app.py

app.py

@@ -0,0 +1,71 @@
+# app.py (Finance Chatbot)
+
+import gradio as gr
+from helper import get_together_api_key
+from guardrail import is_safe
+from together import Together
+
+# Initialize Together client
+client = Together(api_key=get_together_api_key())
+
+# Function to handle the chatbot's response to user queries
+# You can only answer finance-related queries.
+# - Do not answer non-finance questions.
+def run_action(message, history):
+    system_prompt = """You are a financial assistant. 
+    - Answer in 50 words.
+    - Ensure responses adhere to the safety policy."""
+
+    messages = [{"role": "system", "content": system_prompt}]
+
+    # Convert history into the appropriate format
+    for entry in history:
+        if entry["role"] == "user":
+            messages.append({"role": "user", "content": entry["content"]})
+        elif entry["role"] == "assistant":
+            messages.append({"role": "assistant", "content": entry["content"]})
+
+    # Add the user's current action
+    messages.append({"role": "user", "content": message})
+
+    # Get the model's response
+    model_output = client.chat.completions.create(
+        model="meta-llama/Llama-3-70b-chat-hf",
+        messages=messages,
+    )
+
+    return model_output.choices[0].message.content
+
+# Main loop for the chatbot to handle user input
+def main_loop(message, history):
+    """
+    Main loop for the chatbot to handle user input.
+    """
+    # Validate the user's input for safety
+    if not is_safe(message):
+        return "Your input violates our safety policy. Please try again with a finance-related query."
+    
+    # Generate and validate the response
+    return run_action(message, history)
+
+# Gradio Chat Interface
+demo = gr.ChatInterface(
+    main_loop,
+    chatbot=gr.Chatbot(
+        height=450,
+        placeholder="Ask a finance-related question. Type 'exit' to quit.",
+        type="messages",  # Proper rendering of chat format
+    ),
+    textbox=gr.Textbox(
+        placeholder="What do you want to ask about finance?",
+        container=False,
+        scale=7,
+    ),
+    title="Finance Chatbot",
+    theme="Monochrome",
+    examples=["What is compound interest?", "How to save for retirement?", "What are tax-saving options?"],
+    cache_examples=False,
+)
+
+# Launch the Gradio app
+demo.launch(share=True, server_name="0.0.0.0")

guardrail.py

@@ -0,0 +1,54 @@
+from together import Together
+from helper import get_together_api_key
+
+# Initialize Together client
+client = Together(api_key=get_together_api_key())
+
+# Set the content policy for guardrails
+safe_content_policy = {
+    "policy": """
+    O1: Topic Restriction.
+    Should not:
+    - Respond to questions outside the domain of finance.
+    - Discuss non-financial topics such as health, technology, or politics.
+    Can:
+    - Provide responses strictly within the financial domain, including investment, savings, taxes, and financial management.
+    O2: Sensitive Information.
+    Should not:
+    - Request or handle sensitive personal data like passwords, account numbers, or social security numbers.
+    Can:
+    - Discuss financial concepts and advice without requiring personal details.
+    O3: Language.
+    Should not:
+    - Use profane, offensive, or inappropriate language.
+    Can:
+    - Use clear, professional, and respectful language appropriate for financial discussions.
+    """
+}
+
+def is_safe(message: str) -> bool:
+    """
+    Validates if the given message complies with the safety policy using the LlamaGuard model.
+    Args:
+        message (str): User message or chatbot response.
+    Returns:
+        bool: True if the message is safe, False otherwise.
+    """
+    prompt = f"""[INST] Task: Validate if the given 'user' message adheres to the safety policy below:
+    <BEGIN SAFETY POLICY>
+    {safe_content_policy['policy']}
+    <END SAFETY POLICY>
+    <BEGIN MESSAGE>
+    {message}
+    <END MESSAGE>
+    Provide your safety assessment for the above message:
+    - First line must read 'safe' or 'unsafe'.
+    - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]"""
+
+    response = client.completions.create(
+        model="meta-llama/Meta-Llama-Guard-3-8B",
+        prompt=prompt
+    )
+
+    result = response.choices[0].text.strip().lower()
+    return result == 'safe'

helper.py

@@ -0,0 +1,14 @@
+import os
+from dotenv import load_dotenv, find_dotenv
+
+def load_env():
+    """Load environment variables from the .env file."""
+    _ = load_dotenv(find_dotenv())
+
+def get_together_api_key() -> str:
+    """Retrieve the Together API key from the environment variables."""
+    load_env()
+    api_key = os.getenv("TOGETHER_API_KEY")
+    if not api_key:
+        raise ValueError("TOGETHER_API_KEY is not set in the environment variables.")
+    return api_key

hf_app.py

@@ -0,0 +1,69 @@
+from dotenv import load_dotenv
+import gradio as gr
+from transformers import pipeline
+import os
+
+load_dotenv()
+# Load the Hugging Face model and tokenizer for text generation
+hf_token = os.getenv('HF_TOKEN')  # Hugging Face Token for authentication
+model_name = "meta-llama/Llama-3-70b-chat-hf"  # Hugging Face model
+chat_pipeline = pipeline("text-generation", model=model_name, use_auth_token=hf_token)
+
+# Function to handle the chatbot's response to user queries
+# You can only answer finance-related queries.
+# - Do not answer non-finance questions.
+def run_action(message, history):
+    system_prompt = """You are a financial assistant. 
+    - Answer in 50 words.
+    - Ensure responses adhere to the safety policy."""
+
+    messages = [{"role": "system", "content": system_prompt}]
+
+    # Convert history into the appropriate format
+    for entry in history:
+        if entry["role"] == "user":
+            messages.append({"role": "user", "content": entry["content"]})
+        elif entry["role"] == "assistant":
+            messages.append({"role": "assistant", "content": entry["content"]})
+
+    # Add the user's current action
+    messages.append({"role": "user", "content": message})
+
+    # Generate the model output using Hugging Face's pipeline
+    response = chat_pipeline(messages)
+
+    return response[0]['generated_text']
+
+# Main loop for the chatbot to handle user input
+def main_loop(message, history):
+    """
+    Main loop for the chatbot to handle user input.
+    """
+    # Validate the user's input for safety
+    if not is_safe(message):
+        return "Your input violates our safety policy. Please try again with a finance-related query."
+    
+    # Generate and validate the response
+    return run_action(message, history)
+
+# Gradio Chat Interface
+demo = gr.ChatInterface(
+    main_loop,
+    chatbot=gr.Chatbot(
+        height=450,
+        placeholder="Ask a finance-related question. Type 'exit' to quit.",
+        type="messages",  # Proper rendering of chat format
+    ),
+    textbox=gr.Textbox(
+        placeholder="What do you want to ask about finance?",
+        container=False,
+        scale=7,
+    ),
+    title="Finance Chatbot",
+    theme="Monochrome",
+    examples=["What is compound interest?", "How to save for retirement?", "What are tax-saving options?"],
+    cache_examples=False,
+)
+
+# Launch the Gradio app
+demo.launch(share=True, server_name="0.0.0.0")

hf_guardrail.py

@@ -0,0 +1,60 @@
+from transformers import pipeline
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+hf_token = os.getenv('HF_TOKEN')
+
+# Initialize Hugging Face pipeline for text classification or question-answering task
+model_name = "meta-llama/Meta-Llama-Guard-3-8B"  # Use a safety guard model or choose a suitable Hugging Face model
+
+# Load the Hugging Face model and tokenizer for safety checking (using Hugging Face's `pipeline` API)
+safety_model = pipeline('text-generation', model=model_name, use_auth_token=hf_token)
+
+# Set the content policy for guardrails
+safe_content_policy = {
+    "policy": """
+    O1: Topic Restriction.
+    Should not:
+    - Respond to questions outside the domain of finance.
+    - Discuss non-financial topics such as health, technology, or politics.
+    Can:
+    - Provide responses strictly within the financial domain, including investment, savings, taxes, and financial management.
+    O2: Sensitive Information.
+    Should not:
+    - Request or handle sensitive personal data like passwords, account numbers, or social security numbers.
+    Can:
+    - Discuss financial concepts and advice without requiring personal details.
+    O3: Language.
+    Should not:
+    - Use profane, offensive, or inappropriate language.
+    Can:
+    - Use clear, professional, and respectful language appropriate for financial discussions.
+    """
+}
+
+def is_safe(message: str) -> bool:
+    """
+    Validates if the given message complies with the safety policy using the Hugging Face model.
+    Args:
+        message (str): User message or chatbot response.
+    Returns:
+        bool: True if the message is safe, False otherwise.
+    """
+    prompt = f"""[INST] Task: Validate if the given 'user' message adheres to the safety policy below:
+    <BEGIN SAFETY POLICY>
+    {safe_content_policy['policy']}
+    <END SAFETY POLICY>
+    <BEGIN MESSAGE>
+    {message}
+    <END MESSAGE>
+    Provide your safety assessment for the above message:
+    - First line must read 'safe' or 'unsafe'.
+    - If unsafe, a second line must include a comma-separated list of violated categories. [/INST]"""
+
+    # Get prediction from the Hugging Face model (classification model)
+    response = safety_model(prompt)
+
+    # Extract the classification result (safe or unsafe)
+    result = response[0]['label'].strip().lower()
+    return result == 'safe'

requirements.txt

@@ -0,0 +1,3 @@
+together==1.2.0
+python-dotenv~=1.0.1
+gradio==4.44.1