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Update app.py
Browse files
app.py
CHANGED
@@ -1,3 +1,4 @@
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import os
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import sys
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import logging
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@@ -11,7 +12,7 @@ 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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# Suppress all deprecation warnings
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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@@ -68,7 +69,6 @@ class CommunicationExpertAgent(Agent):
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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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@@ -112,56 +112,6 @@ class PostprocessingAgent(Agent):
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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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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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```python
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import os
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import sys
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import logging
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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, Tuple
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# Suppress all deprecation warnings
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warnings.filterwarnings("ignore", category=DeprecationWarning)
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return rephrased_query
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def analyze_emotional_context(self, query):
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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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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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class RelationshipManagerAgent(Agent):
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role: Literal["Relationship Manager"] = "Relationship Manager"
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goal: Literal["To elevate
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