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app.py

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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,
+)

requirements.txt

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+streamlit
+pandas
+numpy
+groq
+streamlit-extras