app.py

import streamlit as st
from crewai import Agent, Task, Crew, Process
import os
from crewai_tools import ScrapeWebsiteTool, SerperDevTool
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from docx import Document
from io import BytesIO
import base64

load_dotenv()

# LLM object and API Key
os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY")
os.environ["SERPER_API_KEY"] = os.getenv("SERPER_API_KEY")


def generate_docx(result):
    doc = Document()
    doc.add_heading('Healthcare Diagnosis and Treatment Recommendations', 0)
    doc.add_paragraph(result)
    bio = BytesIO()
    doc.save(bio)
    bio.seek(0)
    return bio

def get_download_link(bio, filename):
    b64 = base64.b64encode(bio.read()).decode()
    return f'<a href="data:application/vnd.openxmlformats-officedocument.wordprocessingml.document;base64,{b64}" download="{filename}">Download Diagnosis and Treatment Plan</a>'

st.set_page_config(
    layout="wide"
)

# Title
st.title("AI Agents to Empower Doctors")


# Text Inputs
gender = st.selectbox('Select Gender', ('Male', 'Female', 'Other'))
age = st.number_input('Enter Age', min_value=0, max_value=120, value=25)
symptoms = st.text_area('Enter Symptoms', 'e.g., fever, cough, headache')
medical_history = st.text_area('Enter Medical History', 'e.g., diabetes, hypertension')

# Initialize Tools
search_tool = SerperDevTool()
scrape_tool = ScrapeWebsiteTool()

llm = ChatOpenAI(
    model="gpt-3.5-turbo-16k",
    temperature=0.1,
    max_tokens=8000
)

# Define Agents
diagnostician = Agent(
    role="Medical Diagnostician",
    goal="Analyze patient symptoms and medical history to provide a preliminary diagnosis.",
    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.",
    verbose=True,
    allow_delegation=False,
    tools=[search_tool, scrape_tool],
    llm=llm
)

treatment_advisor = Agent(
    role="Treatment Advisor",
    goal="Recommend appropriate treatment plans based on the diagnosis provided by the Medical Diagnostician.",
    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.",
    verbose=True,
    allow_delegation=False,
    tools=[search_tool, scrape_tool],
    llm=llm
)

# Define Tasks
diagnose_task = Task(
    description=(
        "1. Analyze the patient's symptoms ({symptoms}) and medical history ({medical_history}).\n"
        "2. Provide a preliminary diagnosis with possible conditions based on the provided information.\n"
        "3. Limit the diagnosis to the most likely conditions."
    ),
    expected_output="A preliminary diagnosis with a list of possible conditions.",
    agent=diagnostician
)

treatment_task = Task(
    description=(
        "1. Based on the diagnosis, recommend appropriate treatment plans step by step.\n"
        "2. Consider the patient's medical history ({medical_history}) and current symptoms ({symptoms}).\n"
        "3. Provide detailed treatment recommendations, including medications, lifestyle changes, and follow-up care."
    ),
    expected_output="A comprehensive treatment plan tailored to the patient's needs.",
    agent=treatment_advisor
)

# Create Crew
crew = Crew(
    agents=[diagnostician, treatment_advisor],
    tasks=[diagnose_task, treatment_task],
    verbose=2
)

# Execution
if st.button("Get Diagnosis and Treatment Plan"):
    with st.spinner('Generating recommendations...'):
        result = crew.kickoff(inputs={"symptoms": symptoms, "medical_history": medical_history})
        st.write(result)
        docx_file = generate_docx(result)

        download_link = get_download_link(docx_file, "diagnosis_and_treatment_plan.docx")
        
        st.markdown(download_link, unsafe_allow_html=True)