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Create app.py
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app.py
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import streamlit as st
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from crewai import Agent, Task, Crew, Process
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import os
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from crewai_tools import ScrapeWebsiteTool, SerperDevTool
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from docx import Document
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from io import BytesIO
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import base64
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load_dotenv()
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# LLM object and API Key
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os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
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os.environ["SERPER_API_KEY"] = os.getenv("SERPER_API_KEY")
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def generate_docx(result):
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doc = Document()
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doc.add_heading('Healthcare Diagnosis and Treatment Recommendations', 0)
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doc.add_paragraph(result)
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bio = BytesIO()
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doc.save(bio)
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bio.seek(0)
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return bio
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def get_download_link(bio, filename):
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b64 = base64.b64encode(bio.read()).decode()
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return f'<a href="data:application/vnd.openxmlformats-officedocument.wordprocessingml.document;base64,{b64}" download="{filename}">Download Diagnosis and Treatment Plan</a>'
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st.set_page_config(
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layout="wide"
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)
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# Title
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st.title("AI Agents to Empower Doctors")
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# Text Inputs
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gender = st.selectbox('Select Gender', ('Male', 'Female', 'Other'))
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age = st.number_input('Enter Age', min_value=0, max_value=120, value=25)
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symptoms = st.text_area('Enter Symptoms', 'e.g., fever, cough, headache')
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medical_history = st.text_area('Enter Medical History', 'e.g., diabetes, hypertension')
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# Initialize Tools
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search_tool = SerperDevTool()
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scrape_tool = ScrapeWebsiteTool()
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llm = ChatOpenAI(
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model="gpt-3.5-turbo-16k",
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temperature=0.1,
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max_tokens=8000
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)
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# Define Agents
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diagnostician = Agent(
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role="Medical Diagnostician",
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goal="Analyze patient symptoms and medical history to provide a preliminary diagnosis.",
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backstory="This agent specializes in diagnosing medical conditions based on patient-reported symptoms and medical history. It uses advanced algorithms and medical knowledge to identify potential health issues.",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, scrape_tool],
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llm=llm
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)
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treatment_advisor = Agent(
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role="Treatment Advisor",
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goal="Recommend appropriate treatment plans based on the diagnosis provided by the Medical Diagnostician.",
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backstory="This agent specializes in creating treatment plans tailored to individual patient needs. It considers the diagnosis, patient history, and current best practices in medicine to recommend effective treatments.",
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verbose=True,
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allow_delegation=False,
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tools=[search_tool, scrape_tool],
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llm=llm
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)
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# Define Tasks
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diagnose_task = Task(
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description=(
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"1. Analyze the patient's symptoms ({symptoms}) and medical history ({medical_history}).\n"
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"2. Provide a preliminary diagnosis with possible conditions based on the provided information.\n"
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"3. Limit the diagnosis to the most likely conditions."
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),
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expected_output="A preliminary diagnosis with a list of possible conditions.",
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agent=diagnostician
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)
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treatment_task = Task(
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description=(
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"1. Based on the diagnosis, recommend appropriate treatment plans step by step.\n"
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"2. Consider the patient's medical history ({medical_history}) and current symptoms ({symptoms}).\n"
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"3. Provide detailed treatment recommendations, including medications, lifestyle changes, and follow-up care."
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),
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expected_output="A comprehensive treatment plan tailored to the patient's needs.",
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agent=treatment_advisor
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)
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# Create Crew
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crew = Crew(
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agents=[diagnostician, treatment_advisor],
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tasks=[diagnose_task, treatment_task],
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verbose=2
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)
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# Execution
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if st.button("Get Diagnosis and Treatment Plan"):
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with st.spinner('Generating recommendations...'):
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result = crew.kickoff(inputs={"symptoms": symptoms, "medical_history": medical_history})
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st.write(result)
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docx_file = generate_docx(result)
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download_link = get_download_link(docx_file, "diagnosis_and_treatment_plan.docx")
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st.markdown(download_link, unsafe_allow_html=True)
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