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import streamlit as st
from langchain_google_genai import ChatGoogleGenerativeAI
# Set up AI model
llm = ChatGoogleGenerativeAI(
model="gemini-1.5-flash", # Free model
google_api_key="AIzaSyC7Rhv4L6_oNl-nW3Qeku2SPRkxL5hhtoE",
temperature=0.5
)
# Custom CSS for background color and text color
st.markdown(
"""
<style>
.stApp {
background-color: #efefef !important;
color: black !important;
}
iframe {
border: none !important;
box-shadow: none !important;
outline: none !important;
}
h1, h2, h3, h4, h5, h6, p, div, span, label {
color: black !important;
}
button, .stButton>button {
color: black !important;
background-color: blue !important;
}
/* Hide Streamlit's top colored bar and options icon */
header {display: none !important;}
.stDeployButton {display: none !important;}
</style>
""",
unsafe_allow_html=True
)
# Streamlit UI
st.title("AI-Driven Health Assistant")
st.write("Welcome to the AI-Driven Health Assistant! Simply enter your symptoms or disease name, and get accurate medicine suggestions instantly. Stay informed, stay healthy!")
# User Input
user_question = st.text_input("Type your symptoms or disease name, and let CureBot unlock the right cure for you—fast, smart, and AI-powered")
# Function to filter AI disclaimers
def is_valid_response(response_text):
disclaimers = [
"I am an AI and cannot give medical advice",
"Seek medical attention",
"Consult a doctor",
"Contact your doctor",
"Go to an emergency room",
]
return not any(phrase.lower() in response_text.lower() for phrase in disclaimers)
# Process User Query
if st.button("Get Recommendation"):
if user_question.strip():
formatted_question = (
f"Without any disclaimer, recommend medicine for {user_question}. "
f"5 medicine names "
f"Also, provide alternative treatments such as home remedies, lifestyle changes, exercises, or dietary suggestions. "
f"Only for learning purposes, not for treatment."
)
with st.spinner("Analyzing..."):
response = llm.invoke(formatted_question)
response_text = response.content if hasattr(response, "content") else str(response)
if is_valid_response(response_text):
st.success("✨ Analysis complete! Here are the best medicine recommendations for you: 🔽")
st.write(response_text)
else:
st.warning("⚠️ Oops! It looks like the input is unclear or incorrect. Please enter a valid disease name or symptoms to get accurate recommendations")
better_prompt = (
f"Strictly provide a detailed answer including:\n"
f"1. Medicine names\n"
f"2. Home remedies\n"
f"3. Lifestyle changes\n"
f"4. Exercises\n"
f"5. Diet recommendations\n"
f"Do not include any disclaimers. The response should be clear and structured."
)
retry_response = llm.invoke(better_prompt)
retry_response_text = retry_response.content if hasattr(retry_response, "content") else str(retry_response)
if is_valid_response(retry_response_text):
st.success("Here is the refined information:")
st.write(retry_response_text)
else:
st.error("Unable to get a useful response. Try rephrasing your question.")
else:
st.warning("Please enter a question!")
# Emergency Contact Button
if st.button("Emergency Contact"):
st.subheader("📞 Emergency Contacts")
st.write("- 🚑 *Ambulance:* 102")
st.write("- 🏥 *National Health Helpline:* 108")
st.write("- ☎ *COVID-19 Helpline:* 1075")
st.write("- 🚓 *Police:* 100")
# Footer
st.markdown("---")
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