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Update app.py
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app.py
CHANGED
@@ -12,90 +12,10 @@ from crewai import Agent as CrewAgent, Task, Crew
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import autogen
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from langchain_openai import ChatOpenAI
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#
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# Check for OpenAI API key
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if 'OPENAI_API_KEY' not in os.environ:
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logger.error("OPENAI_API_KEY environment variable is not set.")
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logger.info("Please set the OPENAI_API_KEY environment variable before running this script.")
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sys.exit(1)
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# Initialize the client with the Mistral-7B-Instruct-v0.2 model
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try:
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client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")
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except Exception as e:
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logger.error(f"Failed to initialize InferenceClient: {e}")
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sys.exit(1)
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# Shared context for both agents
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SHARED_CONTEXT = """You are part of a multi-agent system designed to provide respectful, empathetic, and accurate support for Zerodha, a leading Indian financial services company. Your role is crucial in ensuring all interactions uphold the highest standards of customer service while maintaining Zerodha's excellent reputation.
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Key points about Zerodha:
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1. India's largest discount broker, known for innovative technology and low-cost trading.
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2. Flat fee structure: ₹20 per executed order for intraday and F&O trades, zero brokerage for delivery equity investments.
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3. Main trading platform: Kite (web and mobile).
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4. Coin platform for commission-free direct mutual fund investments.
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5. Extensive educational resources through Varsity.
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6. Additional tools: Sentinel (price alerts) and ChartIQ (advanced charting).
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7. Console for account management and administrative tasks.
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Always prioritize user safety, ethical investing practices, and transparent communication. Never provide information that could mislead users or bring disrepute to Zerodha."""
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# Guardrail functions
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def sanitize_input(input_text):
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return re.sub(r'[<>&\']', '', input_text)
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approved_topics = ['account opening', 'trading', 'fees', 'platforms', 'funds', 'regulations', 'support']
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vectorizer = CountVectorizer()
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classifier = MultinomialNB()
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X = vectorizer.fit_transform(approved_topics)
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y = np.arange(len(approved_topics))
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classifier.fit(X, y)
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def is_relevant_topic(query):
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query_vector = vectorizer.transform([query])
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prediction = classifier.predict(query_vector)
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return prediction[0] in range(len(approved_topics))
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def redact_sensitive_info(text):
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text = re.sub(r'\b\d{10,12}\b', '[REDACTED]', text)
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text = re.sub(r'[A-Z]{5}[0-9]{4}[A-Z]', '[REDACTED]', text)
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return text
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def check_response_content(response):
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unauthorized_patterns = [
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r'\b(guarantee|assured|certain)\b.*\b(returns|profit)\b',
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r'\b(buy|sell)\b.*\b(specific stocks?|shares?)\b'
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]
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return not any(re.search(pattern, response, re.IGNORECASE) for pattern in unauthorized_patterns)
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def check_confidence(response):
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uncertain_phrases = ["I'm not sure", "It's possible", "I don't have enough information"]
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return not any(phrase.lower() in response.lower() for phrase in uncertain_phrases)
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async def generate_response(prompt):
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try:
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return await client.text_generation(prompt, max_new_tokens=500, temperature=0.7)
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except Exception as e:
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logger.error(f"Error generating response: {e}")
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return "I apologize, but I'm having trouble generating a response at the moment. Please try again later."
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def post_process_response(response):
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response = re.sub(r'\b(stupid|dumb|idiotic|foolish)\b', 'mistaken', response, flags=re.IGNORECASE)
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if not re.search(r'(Thank you|Is there anything else|Hope this helps|Let me know if you need more information)\s*$', response, re.IGNORECASE):
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response += "\n\nIs there anything else I can help you with regarding Zerodha's services?"
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if re.search(r'\b(invest|trade|buy|sell|market)\b', response, re.IGNORECASE):
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response += "\n\nPlease note that this information is for educational purposes only and should not be considered as financial advice. Always do your own research and consider consulting with a qualified financial advisor before making investment decisions."
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return response
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# CrewAI and AutoGen setup
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chat_model = ChatOpenAI(model="gpt-3.5-turbo")
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communication_expert_crew = CrewAgent(
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role='Communication Expert',
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@@ -164,25 +84,68 @@ async def zerodha_support(message, history):
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sanitized_message = redact_sensitive_info(sanitized_message)
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# Use crewAI for initial query rephrasing
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# Use AutoGen for generating the response
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await
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import autogen
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from langchain_openai import ChatOpenAI
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# ... (previous code remains the same)
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# Modify the CrewAI and AutoGen setup
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chat_model = ChatOpenAI(model_name="gpt-3.5-turbo")
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communication_expert_crew = CrewAgent(
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role='Communication Expert',
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sanitized_message = redact_sensitive_info(sanitized_message)
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# Use crewAI for initial query rephrasing
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try:
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rephrase_task = Task(
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description=f"Rephrase the following user query with empathy and respect: '{sanitized_message}'",
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agent=communication_expert_crew
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)
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crew = Crew(
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agents=[communication_expert_crew],
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tasks=[rephrase_task],
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verbose=2
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)
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rephrased_query = crew.kickoff()
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except Exception as e:
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logger.error(f"Error in CrewAI rephrasing: {e}")
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rephrased_query = sanitized_message # Fallback to original message if rephrasing fails
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# Use AutoGen for generating the response
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try:
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response = await get_autogen_response(rephrased_query)
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except Exception as e:
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logger.error(f"Error in AutoGen response generation: {e}")
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response = "I apologize, but I'm having trouble generating a response at the moment. Please try again later."
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if not check_response_content(response):
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response += "\n\nPlease note that I cannot provide specific investment advice or guarantee returns. For personalized guidance, please consult with a qualified financial advisor."
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if not check_confidence(response):
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return "I apologize, but I'm not confident in providing an accurate answer to this query. For the most up-to-date and accurate information, please contact Zerodha's customer support directly."
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final_response = post_process_response(response)
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return final_response
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except Exception as e:
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logger.error(f"Error in zerodha_support: {e}")
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return "I apologize, but an error occurred while processing your request. Please try again later."
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async def get_autogen_response(query):
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await user_proxy.a_initiate_chat(
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response_expert_autogen,
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message=f"Please provide a respectful and empathetic response to the following query: '{query}'"
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)
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return response_expert_autogen.last_message()["content"]
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# Gradio interface setup
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demo = gr.ChatInterface(
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zerodha_support,
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chatbot=gr.Chatbot(height=600),
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textbox=gr.Textbox(placeholder="Ask your question about Zerodha here...", container=False, scale=7),
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title="Zerodha Support Assistant",
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description="Ask questions about Zerodha's services, trading, account management, and more. Our multi-agent system ensures respectful and empathetic responses.",
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theme="soft",
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examples=[
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"How do I open a Zerodha account?",
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"I'm frustrated with the recent changes to the Kite platform. Can you help?",
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"What are the risks involved in F&O trading?",
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"I think there's an error in my account statement. What should I do?",
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"Can you explain Zerodha's policy on intraday trading margins?",
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"I'm new to investing. What resources does Zerodha offer for beginners?",
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"How does Zerodha ensure the security of my investments and personal data?"
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],
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)
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if __name__ == "__main__":
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demo.launch()
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