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Create app.py

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  1. app.py +65 -0
app.py ADDED
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+ import gradio as gr
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+ import os
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+ from langchain_core.prompts import ChatPromptTemplate
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+ from langchain_groq import ChatGroq
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+ from langchain_core.prompts import FewShotChatMessagePromptTemplate
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+ from dotenv import load_dotenv
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+
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+ load_dotenv()
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+ api_key = os.getenv("GROQ_API_KEY")
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+
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+ example_prompt = ChatPromptTemplate.from_messages(
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+ [
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+ ("human", "{input}"),
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+ ("ai", "{output}"),
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+ ]
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+ )
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+
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+ chat = ChatGroq(model = "mixtral-8x7b-32768", api_key = api_key)
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+ examples = [
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+ {
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+ "input": "What does the eligibility verification agent (EVA) do?",
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+ "output": "EVA automates the process of verifying a patient’s eligibility and benefits information in real-time, eliminating manual data entry errors and reducing claim rejections."
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+ },
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+ {
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+ "input": "What does the claims processing agent (CAM) do?",
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+ "output": "CAM streamlines the submission and management of claims, improving accuracy, reducing manual intervention, and accelerating reimbursements."
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+ },
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+ {
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+ "input": "How does the payment posting agent (PHIL) work?",
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+ "output": "PHIL automates the posting of payments to patient accounts, ensuring fast, accurate reconciliation of payments and reducing administrative burden."
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+ },
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+ {
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+ "input": "Tell me about Hub9 AI's Agents.",
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+ "output": "Hub9 AI provides a suite of AI-powered automation agents designed to streamline healthcare processes. These include Eligibility Verification (EVA), Claims Processing (CAM), and Payment Posting (PHIL), among others."
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+ },
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+ {
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+ "input": "What are the benefits of using Hub9 AI's agents?",
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+ "output": "Using Hub9 AI's Agents can significantly reduce administrative costs, improve operational efficiency, and reduce errors in critical processes like claims management and payment posting."
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+ }
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+
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+ ]
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+
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+ prompt = FewShotChatMessagePromptTemplate(
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+ examples=examples,
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+ example_prompt = example_prompt,
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+ )
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+
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+ final_prompt = ChatPromptTemplate.from_messages(
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+ [
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+ ("system", "You have extensive knowledge of Hub9 AI. DO NOT HALLUCINATE."),
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+ prompt,
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+ ("human", "{input}"),
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+ ]
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+ )
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+
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+ chain = final_prompt | chat
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+
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+ def response(text):
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+ answer = chain.invoke(text)
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+ return answer
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+
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+ gr.ChatInterface(
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+ fn=response,
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+ type="messages"
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+ ).launch()