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Update app.py
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app.py
CHANGED
@@ -9,7 +9,6 @@ from sklearn.naive_bayes import MultinomialNB
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import asyncio
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from crewai import Agent
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from huggingface_hub import InferenceClient
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import random
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import json
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import warnings
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from typing import Literal
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@@ -63,11 +62,20 @@ class CommunicationExpertAgent(Agent):
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sanitized_query = re.sub(r'[<>&\']', '', query)
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topic_relevance = classifier.predict(vectorizer.transform([sanitized_query]))[0] in range(len(approved_topics))
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if not topic_relevance:
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return "
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emotional_context =
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rephrased_query = f"Rephrased with empathy: {sanitized_query}
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return rephrased_query
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class ResponseExpertAgent(Agent):
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role: Literal["Response Expert"] = "Response Expert"
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goal: Literal["To provide accurate, helpful, and emotionally intelligent responses to user queries"] = "To provide accurate, helpful, and emotionally intelligent responses to user queries"
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@@ -77,14 +85,11 @@ class ResponseExpertAgent(Agent):
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async def run(self, rephrased_query):
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try:
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logger.info(f"Sending query for generation: {rephrased_query}")
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response =
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return response['generated_text']
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else:
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return "Failed to retrieve generated text from response."
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except Exception as e:
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logger.error(f"Failed to generate text due to: {str(e)}")
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return "
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class PostprocessingAgent(Agent):
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role: Literal["Postprocessing Expert"] = "Postprocessing Expert"
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@@ -93,7 +98,18 @@ class PostprocessingAgent(Agent):
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"You are responsible for finalizing communications, adding polite terminations, and ensuring that the responses meet the quality standards expected in customer interactions."
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def run(self, response):
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return response
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# Instantiate agents
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@@ -102,26 +118,50 @@ response_expert = ResponseExpertAgent()
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postprocessing_agent = PostprocessingAgent()
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async def handle_query(query):
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# Gradio interface setup
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def setup_interface():
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with gr.Blocks() as app:
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with gr.Row():
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query_input = gr.Textbox(label="Enter your query")
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submit_button = gr.Button("Submit")
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submit_button.click(
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fn=lambda x: asyncio.run(handle_query(x)),
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inputs=[query_input],
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outputs=[response_output]
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)
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return app
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app = setup_interface()
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if __name__ == "__main__":
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app.launch()
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import asyncio
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from crewai import Agent
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from huggingface_hub import InferenceClient
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import json
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import warnings
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from typing import Literal
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sanitized_query = re.sub(r'[<>&\']', '', query)
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topic_relevance = classifier.predict(vectorizer.transform([sanitized_query]))[0] in range(len(approved_topics))
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if not topic_relevance:
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return "I apologize, but your query doesn't seem to be related to Zerodha's services. Could you please ask about account opening, trading, fees, our platforms, funds, regulations, or customer support?"
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emotional_context = self.analyze_emotional_context(sanitized_query)
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rephrased_query = f"Rephrased query with empathy: {sanitized_query}\nEmotional context: {emotional_context}"
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return rephrased_query
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def analyze_emotional_context(self, query):
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# This is a placeholder. In a real scenario, you'd use sentiment analysis or a more sophisticated method.
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if any(word in query.lower() for word in ['frustrated', 'angry', 'upset']):
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return "The user seems frustrated or upset."
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elif any(word in query.lower() for word in ['confused', 'unclear', 'don\'t understand']):
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return "The user seems confused or seeking clarification."
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else:
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return "The user's emotional state is neutral or unclear."
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class ResponseExpertAgent(Agent):
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role: Literal["Response Expert"] = "Response Expert"
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goal: Literal["To provide accurate, helpful, and emotionally intelligent responses to user queries"] = "To provide accurate, helpful, and emotionally intelligent responses to user queries"
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async def run(self, rephrased_query):
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try:
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logger.info(f"Sending query for generation: {rephrased_query}")
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response = hf_client.text_generation(rephrased_query, max_new_tokens=500, temperature=0.7)
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return response
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except Exception as e:
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logger.error(f"Failed to generate text due to: {str(e)}")
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return "I apologize, but I'm having trouble generating a response at the moment. Please try again or contact Zerodha support directly if the issue persists."
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class PostprocessingAgent(Agent):
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role: Literal["Postprocessing Expert"] = "Postprocessing Expert"
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"You are responsible for finalizing communications, adding polite terminations, and ensuring that the responses meet the quality standards expected in customer interactions."
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def run(self, response):
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processed_response = self.add_disclaimers(response)
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processed_response = self.ensure_politeness(processed_response)
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return processed_response
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def add_disclaimers(self, response):
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if any(word in response.lower() for word in ['invest', 'trade', 'buy', 'sell', 'market']):
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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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def ensure_politeness(self, response):
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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\nThank you for choosing Zerodha. Is there anything else I can assist you with today?"
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return response
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# Instantiate agents
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postprocessing_agent = PostprocessingAgent()
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async def handle_query(query):
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try:
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if not query.strip():
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return "I'm sorry, but I didn't receive any query. Could you please ask a question about Zerodha's services?"
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rephrased_query = await communication_expert.run(query)
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response = await response_expert.run(rephrased_query)
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final_response = postprocessing_agent.run(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 handle_query: {str(e)}")
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return "I apologize, but an error occurred while processing your request. Please try again or contact Zerodha support if the issue persists."
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# Gradio interface setup
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def setup_interface():
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with gr.Blocks() as app:
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gr.Markdown("# Zerodha Support Chatbot")
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gr.Markdown("Ask questions about Zerodha's services, trading, account management, and more.")
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with gr.Row():
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query_input = gr.Textbox(label="Enter your query", placeholder="Type your question here...")
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submit_button = gr.Button("Submit")
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response_output = gr.Textbox(label="Response", lines=10)
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submit_button.click(
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fn=lambda x: asyncio.run(handle_query(x)),
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inputs=[query_input],
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outputs=[response_output]
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)
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gr.Examples(
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examples=[
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"How do I open a Zerodha account?",
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"What are the brokerage charges for intraday trading?",
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"Can you explain how to use the Kite platform?",
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"I'm having trouble logging into my account. What should I do?",
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"What are the margin requirements for F&O trading?"
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],
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inputs=[query_input]
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)
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return app
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app = setup_interface()
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if __name__ == "__main__":
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app.launch()
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