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import streamlit as st
import pandas as pd
from streamlit_extras.colored_header import colored_header
from streamlit_extras.add_vertical_space import add_vertical_space
from groq import Groq
# Initialize Groq API
GROQ_API_KEY = "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"
client = Groq(api_key=GROQ_API_KEY)
# Function to query Groq API
def query_groq(prompt, model="llama3-8b-8192"):
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model=model,
)
return chat_completion.choices[0].message.content
# Streamlit UI
st.set_page_config(
page_title="Crop Yield Insights",
page_icon="🌾",
layout="wide",
)
# Sidebar
with st.sidebar:
st.image("https://cdn-icons-png.flaticon.com/512/868/868909.png", width=120)
st.title("Crop Yield Assistant 🌱")
st.markdown("Get recommendations and predictions for crops based on data insights.")
add_vertical_space(3)
st.info("Upload your CSV file to get started!")
# Main app
st.title("🌾 Crop Yield Insights")
st.markdown("Upload your dataset and select an ID to get relevant insights, predictions, and recommendations.")
# File uploader
uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"], accept_multiple_files=False)
if uploaded_file:
df = pd.read_csv(uploaded_file)
# Data preprocessing
if df.isnull().sum().any():
st.warning("Missing values detected. Filling with median values.")
df.fillna(df.median(numeric_only=True), inplace=True)
# Display dataset preview
st.subheader("📊 Dataset Overview")
st.dataframe(df.head())
# Ensure there is an 'ID' column
if 'ID' not in df.columns:
st.error("The dataset must contain an 'ID' column.")
else:
# Select ID
record_id = st.selectbox("Select an ID:", df['ID'].unique())
if st.button("Generate Insights"):
# Generate insights
record = df[df['ID'] == record_id]
if record.empty:
st.error(f"No record found for ID: {record_id}")
else:
soil_quality = record.iloc[0]['Soil_Quality']
seed_variety = record.iloc[0]['Seed_Variety']
fertilizer_amount = record.iloc[0]['Fertilizer_Amount_kg_per_hectare']
sunny_days = record.iloc[0]['Sunny_Days']
rainfall = record.iloc[0]['Rainfall_mm']
irrigation_schedule = record.iloc[0]['Irrigation_Schedule']
prompt = (
f"The dataset includes the following information for ID {record_id}:\n"
f"- Soil Quality: {soil_quality}\n"
f"- Seed Variety: {seed_variety}\n"
f"- Fertilizer Amount (kg/ha): {fertilizer_amount}\n"
f"- Sunny Days: {sunny_days}\n"
f"- Rainfall (mm): {rainfall}\n"
f"- Irrigation Schedule: {irrigation_schedule}\n\n"
"Using this data, provide insights into expected crop yield, "
"recommendations for improving productivity, and potential challenges."
)
with st.spinner("Fetching insights..."):
response = query_groq(prompt)
st.success(f"🌟 Insights for ID {record_id}")
st.markdown(response)
else:
st.info("Please upload a CSV file to proceed.")
# Footer
st.markdown("---")
st.markdown(
"<h4 style='text-align: center;'>Powered by 🧠 Groq AI | Designed with ❤️ Streamlit</h4>",
unsafe_allow_html=True,
)
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