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import streamlit as st
import base64
import predictor
def add_bg_from_local(image_file):
with open(image_file, "rb") as image_file:
encoded_string = base64.b64encode(image_file.read())
st.markdown(
f"""
<style>
.stApp {{
background-image: url(data:image/{"png"};base64,{encoded_string.decode()});
background-repeat: repeat;
}}
</style>
""",
unsafe_allow_html=True
)
def header_white_bg(text, fontsize = 40, bold = True):
st.markdown(
f"""
<span style="background:rgba(255, 255, 255, 0.8); font-size:{fontsize}px; font-weight:{"bold" if bold else "normal"}">{text}</span>
""",
unsafe_allow_html=True
)
def diagnose_health(file):
prediction = predict(file)
return prediction
def app():
add_bg_from_local('assets/background.png')
header_white_bg(f'<span style="color:green">Plant</span><span style="color:orange">Dx</span><span style="color:green">: Diagnosis in a Snap!</span> ')
# Upload image of plant
header_white_bg("Upload an image of your plant:", fontsize=32)
uploaded_file = st.file_uploader("", type=["jpg", "jpeg", "png"])
if uploaded_file:
header_white_bg("Preview of the selected image:", fontsize=28, bold=False)
st.image(uploaded_file)
# Get diagnosis button
if st.button("Get Diagnosis"):
if uploaded_file is not None:
# Diagnose plant health and display results
result = diagnose_health(uploaded_file)
st.success(f"Your plant is {result}")
else:
st.warning("Please upload an image of your plant first")
# Create user profile button
if st.button("Create User Profile"):
st.subheader("User Profile")
# Prompt user to add their name and the plant they own
user_name = st.text_input("Enter your name:")
plant_name = st.text_input("Enter the plant you own:")
if user_name and plant_name:
st.success(f"User profile created for {user_name} with plant {plant_name}")
# Run Streamlit app
if __name__ == "__main__":
app()
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