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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, | |
| ) | |