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‎.gitignore

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# General
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.DS_Store
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.AppleDouble
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.LSOverride
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.vscode
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.bak
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# Icon must end with two \r
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Icon
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# Thumbnails
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._*
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# Files that might appear in the root of a volume
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.DocumentRevisions-V100
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.fseventsd
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.Spotlight-V100
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.TemporaryItems
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.Trashes
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.VolumeIcon.icns
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.com.apple.timemachine.donotpresent
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# Directories potentially created on remote AFP share
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.AppleDB
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.AppleDesktop
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Network Trash Folder
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Temporary Items
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.apdisk
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# ignore common security keys
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.key
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.crt
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.csr
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.pem
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.gitignore
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shared-assets/bastion-py-script/.oci/
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shared-assets/bastion-py-script/temp/
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temp/
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app-dev/app-integration-and-automation/oracle-integration-cloud/01-oic-connectivity-agent/README_tmp.html

‎ai/gen-ai-agents/README.md

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# Generative AI Agents service
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Oracle’s AI strategy is to make artificial intelligence (AI) pervasive across its cloud applications and cloud infrastructure. We serve business users who want to improve business processes and outcomes through prebuilt AI capabilities, data scientists and developers who want complete control to build and deploy AI models of any kind, and independent software vendors (ISVs) who want the most performant and cost-effective platform to host their AI services.
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Reviewed: 13.11.2024
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Oracle’s Generative AI Agents is a fully managed service that combines the power of large language models (LLMs) with an intelligent retrieval system to create contextually relevant answers by searching your knowledge base, making your AI applications smart and efficient. The service provides up-to-date information through a natural language interface and the ability to act directly on it.
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# Team Publications
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- [Live Lab : Oracle Generative AI Agent: Multi-Format Search. Installation to Advanced Techniques](https://livelabs.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=4112)
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- [Live Lab : Build an AI assistant you can access through Slack, Teams and more - Oracle LiveLabs](https://livelabs.oracle.com/pls/apex/dbpm/r/livelabs/view-workshop?wid=4015)
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- [Analysis of Structured Data and Use of AI Agents](https://luigi-saetta.medium.com/analysis-of-structured-data-and-use-of-ai-agents-a40f18da4ed5)
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- [AI Agents: The Future of Intelligent Systems in 2025](https://medium.com/@anshuman4luv/ai-agents-the-future-of-intelligent-systems-in-2025-5558c13aa328)
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- [LangChain vs LangGraph vs LangFlow vs LangSmith: A Detailed Comparison](https://medium.com/@anshuman4luv/langchain-vs-langgraph-vs-langflow-vs-langsmith-a-detailed-comparison-74bc0d7ddaa9)
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## Reusable Assets Overview
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- [HCM agent created by partner Conneqtion Group which contains agents to connect to Fusion HCM, Expense and many others](https://www.youtube.com/watch?v=OhZcWx_H_tQ)
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- [Finance analytics agent created by our partner TPX impact](https://bit.ly/genai4analyst)
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# Useful Links
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- [AI Solutions Hub](https://www.oracle.com/artificial-intelligence/solutions/)
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- [GEN AI Agents](https://docs.oracle.com/en-us/iaas/Content/generative-ai-agents/home.htm)
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- [AI Agents Overview](https://docs.oracle.com/en-us/iaas/Content/generative-ai-agents/overview.htm)
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Reviewed: 28.03.2025
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# License

‎ai/gen-ai-agents/assistant-secretary-agent/LICENSE

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Copyright (c) 2025 Oracle and/or its affiliates.
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The Universal Permissive License (UPL), Version 1.0
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Subject to the condition set forth below, permission is hereby granted to any
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person obtaining a copy of this software, associated documentation and/or data
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(collectively the "Software"), free of charge and under any and all copyright
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rights in the Software, and any and all patent rights owned or freely
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licensable by each licensor hereunder covering either (i) the unmodified
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Software as contributed to or provided by such licensor, or (ii) the Larger
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Works (as defined below), to deal in both
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(a) the Software, and
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(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
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one is included with the Software (each a "Larger Work" to which the Software
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is contributed by such licensors),
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without restriction, including without limitation the rights to copy, create
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derivative works of, display, perform, and distribute the Software and make,
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use, sell, offer for sale, import, export, have made, and have sold the
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Software and the Larger Work(s), and to sublicense the foregoing rights on
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either these or other terms.
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This license is subject to the following condition:
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The above copyright notice and either this complete permission notice or at
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a minimum a reference to the UPL must be included in all copies or
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substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎ai/gen-ai-agents/assistant-secretary-agent/files/assistant.py

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Copyright (c) 2025 Oracle and/or its affiliates.
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"""
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By Omar Salem
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assistant.py - AI Assistant Chatbot (Dynamic Routing).
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"""
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from state import State
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from router import Router
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from tools import (
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handle_fetch_gmail,
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handle_send_email,
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handle_ai_agent_query,
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handle_select_email,
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handle_schedule_email,
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handle_llm_query,
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handle_weather_query,
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handle_calculator,
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# handle_generate_reply_with_weather,
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handle_generate_reply_with_user_message_and_ai_response,
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handle_generate_reply_with_user_message_only,
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handle_book_meeting
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)
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import json
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# initialize router
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router = Router()
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# initialize State
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state: State = {
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"input": "",
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"decisions": [],
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"output": "",
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"emails": [],
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"selected_email": None,
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"recipient": "",
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"subject": "",
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"body": "",
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"ai_response": "",
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"citations": [],
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}
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# dynamically map tools
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TOOLS = {
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"fetch_and_summarize": handle_fetch_gmail,
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"send_email": handle_send_email,
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"ai_agent": handle_ai_agent_query,
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"select_email": handle_select_email,
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"schedule_email": handle_schedule_email,
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"llm_query": handle_llm_query,
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"weather_query": handle_weather_query,
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"calculator": handle_calculator,
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"generate_reply_with_user_message_only": handle_generate_reply_with_user_message_only,
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"generate_reply_with_user_message_and_ai_response": handle_generate_reply_with_user_message_and_ai_response,
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"book_meeting": handle_book_meeting,
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}
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def chatbot():
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"""Interactive Chatbot for AI Assistant"""
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print("\n **AI Assistant** - Type 'exit' to quit.\n")
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while True:
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# get user input
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user_input = input("📝 You: ").strip()
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if user_input.lower() in ["exit", "quit"]:
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print("\n👋 Exiting. Have a great day!")
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break
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# update State
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state["input"] = user_input
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# dynamic routing assignment
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routing_result = router.route(state)
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state["decisions"] = routing_result.get("decisions", [])
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print(f"\n🤖 **Routing Decisions:** `{state['decisions']}`\n")
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# store responses from multiple tools
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responses = []
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#dynamically execute tools
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for decision in state["decisions"]:
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tool = TOOLS.get(decision)
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if tool:
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result = tool(state) # execute tool picked dynamically
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responses.append(result["output"])
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else:
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responses.append(f"❌ Invalid decision: `{decision}`.")
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# convert lists to formatted JSON strings before joining
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state["output"] = "\n\n".join(
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[json.dumps(resp, indent=2) if isinstance(resp, list) else str(resp) for resp in responses]
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)
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# display Output
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print(f"\n✅ **Response:**\n{state['output']}\n")
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# run the Chatbot
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if __name__ == "__main__":
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chatbot()

‎ai/gen-ai-agents/assistant-secretary-agent/files/frontend.py

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Copyright (c) 2025 Oracle and/or its affiliates.
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import streamlit as st
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import json
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import time
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from assistant import TOOLS, router, state
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# ==============================
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# STREAMLIT UI CONFIGURATION
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# ==============================
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st.set_page_config(page_title="OCI Assistant Chatbot", layout="wide")
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st.markdown(
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"<h1 style='text-align: center;'>OCI Assistant Chatbot</h1>",
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unsafe_allow_html=True
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)
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st.write("Manage your emails, schedule meetings, check the weather, and more...")
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# ==============================
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# INITIALIZE SESSION STATE
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# ==============================
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if "chat_history" not in st.session_state:
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st.session_state.chat_history = []
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if "selected_email" not in st.session_state:
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st.session_state.selected_email = None
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if "latest_response" not in st.session_state:
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st.session_state.latest_response = ""
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# ==============================
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# CHATBOT INTERFACE
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# ==============================
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# Display chat history
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for chat in st.session_state.chat_history:
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with st.chat_message("user"):
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st.markdown(chat["user"])
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with st.chat_message("assistant"):
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st.markdown(chat["bot"])
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# User input field
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user_input = st.text_input("Type your message here...", key="user_input")
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# Process user message
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if st.button("Send"):
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if user_input:
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# Display steps
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step_placeholder = st.empty() # This will dynamically update
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step_placeholder.markdown("🟢 **Step 1:** Capturing user input...")
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# Update state with user input
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state["input"] = user_input
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# Display second step
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step_placeholder.markdown("🟢 **Step 2:** Identifying the correct tool...")
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# Show spinner while routing
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with st.spinner("Processing..."):
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time.sleep(1) # Simulating delay
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# Route input to the correct function
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routing_result = router.route(state)
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state["decisions"] = routing_result.get("decisions", [])
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# Show detected decisions
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step_placeholder.markdown(f"🟢 **Step 3:** Decision made → `{state['decisions']}`")
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# Execute tool functions
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responses = []
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for i, decision in enumerate(state["decisions"], start=1):
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step_placeholder.markdown(f"🟢 **Step 4.{i}:** Executing `{decision}`...")
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tool = TOOLS.get(decision)
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if tool:
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with st.spinner(f"Running `{decision}`..."):
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time.sleep(1) # Simulate processing time
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result = tool(state)
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responses.append(result["output"])
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else:
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responses.append(f"❌ Invalid decision: `{decision}`.")
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# Finalize response
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step_placeholder.markdown("✅ **Step 5:** Formatting response...")
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# Store response
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state["output"] = "\n\n".join(
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[json.dumps(resp, indent=2) if isinstance(resp, list) else str(resp) for resp in responses]
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)
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# Update chat history
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st.session_state.chat_history.append({"user": user_input, "bot": state["output"]})
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# Store latest response
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st.session_state.latest_response = state["output"]
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# Refresh UI
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st.rerun()

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