Update app.py

app.py

@@ -1,92 +1,92 @@
-import streamlit as st
-import pickle
-import numpy as np
-import pandas as pd
-from sklearn.preprocessing import LabelEncoder
-import re
-
-# Load Data
-df = pd.read_csv('Crop_Yield.csv')
-cropOptions = list(df['Crop'].unique())
-model_path = 'model.pkl'
-
-css = """
-<style>
-    .stApp  {
-        background-image: url("https://images.pexels.com/photos/265216/pexels-photo-265216.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=2");
-        background-position: center;
-        background-repeat: no-repeat;
-        background-attachment: fixed;
-        margin: 0;
-        padding: 0;
-    }
-    .stForm{
-        background-color: black;
-    }
-    .stButton > button {
-        background-color: white;
-        color: black;
-    }
-</style>
-"""
-
-# Inject custom CSS
-st.markdown(css, unsafe_allow_html=True)
-
-# Load Model
-with open(model_path, 'rb') as file:
-    model = pickle.load(file)
-
-# Initialize Label Encoder and encode columns
-label_encoder_crop = LabelEncoder()
-
-df['Crop_encoded'] = label_encoder_crop.fit_transform(df['Crop'])
-
-# Create mappings for Area and Item
-crop_mapping = dict(zip(label_encoder_crop.classes_, range(len(label_encoder_crop.classes_))))
-
-# Create Form
-with st.form(key="my_form"):
-    st.markdown("<h1 style='text-align: center; background-color: #f4edcd;color:black'>Crop Yield Prediction</h1>", unsafe_allow_html=True)
-    
-    crop = st.selectbox("Choose a Crop:", options=cropOptions)    
-    area = st.text_input("Area (in hectares):")
-    area_error = "" if re.match(r"^\d+(\.\d+)?$", area) or not area else "Invalid input for area. Enter a numeric value without commas or special characters."
-    if area_error:
-        st.markdown(f"<span style='color:red;'>{area_error}</span>", unsafe_allow_html=True)
-    
-    production = st.text_input("Production (in metric tons):")
-    production_error = "" if re.match(r"^\d+(\.\d+)?$", production) or not production else "Invalid input for production. Enter a numeric value without commas or special characters."
-    if production_error:
-        st.markdown(f"<span style='color:red;'>{production_error}</span>", unsafe_allow_html=True)
-    
-    rainfall = st.slider("Annual Rainfall (in mm)", min(df['Annual_Rainfall']), max(df['Annual_Rainfall']), value=min(df['Annual_Rainfall']))
-    fertilizer = st.slider("Fertilizer (in kilograms).", min(df['Fertilizer']), max(df['Fertilizer']), value=min(df['Fertilizer']))
-    pesticide = st.slider("Pesticide (in kilograms).", min(df['Pesticide']), max(df['Pesticide']), value=min(df['Pesticide']))
-    
-    submit_button = st.form_submit_button(label="Predict")
-
-# Handle Form Submission
-if submit_button:
-    # Validate Inputs
-    if area_error or production_error:
-        st.error("Please fix the errors above before proceeding.")
-    else:
-        # Prepare Input Data
-        encoded_crop = crop_mapping[crop]
-
-        input_data = np.array([[pesticide, fertilizer, rainfall,float(production), float(area),  encoded_crop]])
-        
-        # Predict
-        try:
-            prediction = model.predict(input_data)
-            st.markdown(
-                f"""
-                <div style="color: black; font-size: 18px; border: 1px solid darkgreen; border-radius: 5px; padding: 10px; background-color: #e6ffe6;">
-                    <strong>Expected Yield is (production per unit area):</strong> {prediction[0]}
-                </div>
-                """,
-                unsafe_allow_html=True
-            )
-        except Exception as e:
-            st.error(f"Error in prediction: {e}")
+import streamlit as st
+import pickle
+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+import re
+
+# Load Data
+df = pd.read_csv('Crop_Yield.csv')
+cropOptions = list(df['Crop'].unique())
+model_path = 'model.pkl'
+
+css = """
+<style>
+    .stApp  {
+        background-image: url("https://images.pexels.com/photos/265216/pexels-photo-265216.jpeg?auto=compress&cs=tinysrgb&w=1260&h=750&dpr=2");
+        background-position: center;
+        background-repeat: no-repeat;
+        background-attachment: fixed;
+        margin: 0;
+        padding: 0;
+    }
+    .stForm{
+        background-color: black;
+    }
+    .stButton > button {
+        background-color: white;
+        color: black;
+    }
+</style>
+"""
+
+# Inject custom CSS
+st.markdown(css, unsafe_allow_html=True)
+
+# Load Model
+with open(model_path, 'rb') as file:
+    model = pickle.load(file)
+
+# Initialize Label Encoder and encode columns
+label_encoder_crop = LabelEncoder()
+
+df['Crop_encoded'] = label_encoder_crop.fit_transform(df['Crop'])
+
+# Create mappings for Area and Item
+crop_mapping = dict(zip(label_encoder_crop.classes_, range(len(label_encoder_crop.classes_))))
+
+# Create Form
+with st.form(key="my_form"):
+    st.markdown("<h1 style='text-align: center; background-color: #f4edcd;color:black'>Crop Yield Prediction</h1>", unsafe_allow_html=True)
+    
+    crop = st.selectbox("Choose a Crop:", options=cropOptions)    
+    area = st.text_input("Area (in hectares):")
+    area_error = "" if re.match(r"^\d+(\.\d+)?$", area) or not area else "Invalid input for area. Enter a numeric value without commas or special characters."
+    if area_error:
+        st.markdown(f"<span style='color:red;'>{area_error}</span>", unsafe_allow_html=True)
+    
+    production = st.text_input("Production (in metric tons):")
+    production_error = "" if re.match(r"^\d+(\.\d+)?$", production) or not production else "Invalid input for production. Enter a numeric value without commas or special characters."
+    if production_error:
+        st.markdown(f"<span style='color:red;'>{production_error}</span>", unsafe_allow_html=True)
+    
+    rainfall = st.slider("Annual Rainfall (in mm)", min(df['Annual_Rainfall']), max(df['Annual_Rainfall']), value=min(df['Annual_Rainfall']))
+    fertilizer = st.slider("Fertilizer (in kilograms).", min(df['Fertilizer']), max(df['Fertilizer']), value=min(df['Fertilizer']))
+    pesticide = st.slider("Pesticide (in kilograms).", min(df['Pesticide']), max(df['Pesticide']), value=min(df['Pesticide']))
+    
+    submit_button = st.form_submit_button(label="Predict")
+
+# Handle Form Submission
+if submit_button:
+    # Validate Inputs
+    if area_error or production_error:
+        st.error("Please fix the errors above before proceeding.")
+    else:
+        # Prepare Input Data
+        encoded_crop = crop_mapping[crop]
+
+        input_data = np.array([[pesticide, fertilizer, rainfall,float(production), float(area),  encoded_crop]])
+        
+        # Predict
+        try:
+            prediction = model.predict(input_data)
+            st.markdown(
+                f"""
+                <div style="color: black; font-size: 18px; border: 1px solid darkgreen; border-radius: 5px; padding: 10px; background-color: #e6ffe6;">
+                    <strong>Expected Yield is (production per unit area):</strong> {prediction[0]}
+                </div>
+                """,
+                unsafe_allow_html=True
+            )
+        except Exception as e:
+            st.error(f"Error in prediction: {e}")