Update README.md

README.md

@@ -114,13 +114,38 @@ The model expects input data in JSON format with the following fields:
 ### Inference Procedure
 
 ```python
-from joblib import load
+from huggingface_hub import hf_hub_download
 import pandas as pd
+from joblib import load
+import numpy as np
+from sklearn.preprocessing import LabelEncoder
+from googletrans import Translator
+
+# Initialize translator
+translator = Translator()
+
+# Download models from Hugging Face Hub
+preprocessor_path = hf_hub_download(repo_id='your-username/your-repo', filename='preprocessor.joblib')
+numerical_model_path = hf_hub_download(repo_id='your-username/your-repo', filename='numerical_model.joblib')
+categorical_model_path = hf_hub_download(repo_id='your-username/your-repo', filename='categorical_model.joblib')
 
 # Load the preprocessor and trained models
-preprocessor = load('preprocessor.joblib')
-numerical_model = load('numerical_model.joblib')
-categorical_model = load('categorical_model.joblib')
+preprocessor = load(preprocessor_path)
+numerical_model = load(numerical_model_path)
+categorical_model = load(categorical_model_path)
+
+# Define categorical targets (same as used during training)
+categorical_targets = [
+    'Lime Application - Instruction',
+    'Lime Application',
+    'Organic Matter Application - Instruction',
+    'Organic Matter Application',
+    '1st Application',
+    '1st Application - Type fertilizer (1)',
+    '1st Application - Type fertilizer (2)',
+    '2nd Application',
+    '2nd Application - Type fertilizer (1)'
+]
 
 # Example input data
 new_data = {
@@ -145,9 +170,35 @@ numerical_predictions = numerical_model.predict(input_transformed)
 # Make categorical predictions
 categorical_predictions = categorical_model.predict(input_transformed)
 
+# Load label encoders from Hugging Face Hub (if they are saved separately)
+label_encoders = {col: load(hf_hub_download(repo_id='your-username/your-repo', filename=f'label_encoder_{col}.joblib')) for col in categorical_targets}
+
 # Decode categorical predictions
-label_encoders = {col: load(f'label_encoder_{col}.joblib') for col in categorical_targets}
-categorical_predictions_decoded = {col: label_encoders[col].inverse_transform(categorical_predictions[:, i]) for i, col in enumerate(categorical_targets)}
+categorical_predictions_decoded = {}
+for i, col in enumerate(categorical_targets):
+    le = label_encoders[col]
+    try:
+        decoded_labels = le.inverse_transform(categorical_predictions[:, i])
+        # Translate to English
+        translated_labels = [translator.translate(label, dest='en').text for label in decoded_labels]
+        categorical_predictions_decoded[col] = translated_labels
+    except ValueError as e:
+        print(f"Error decoding predictions for {col}: {e}")
+        categorical_predictions_decoded[col] = ["Unknown"] * len(categorical_predictions[:, i])
+
+# Define numerical targets (same as used during training)
+numerical_targets = [
+    'Nitrogen (N) Need',
+    'Phosphorus (P2O5) Need',
+    'Potassium (K2O) Need',
+    'Organic Matter Need',
+    'Lime Need',
+    'Lime Application - Requirement',
+    'Organic Matter Application - Requirement',
+    '1st Application - Requirement (1)',
+    '1st Application - Requirement (2)',
+    '2nd Application - Requirement (1)'
+]
 
 # Combine predictions into a single dictionary
 predictions_combined = {**{col: numerical_predictions[0, i] for i, col in enumerate(numerical_targets)}, **categorical_predictions_decoded}