Update app.py

app.py

@@ -82,31 +82,37 @@
 # if __name__ == '__main__':
 #     app.run(port=7860,host='0.0.0.0')
 
-
 import gradio as gr
 import pandas as pd
 import joblib
 import os
 import google.generativeai as genai
 
-# Load models
+# Load trained models
 rf_ferti_name = joblib.load("rf_ferti_name.pkl")
 rf_ferti_value = joblib.load("rf_ferti_value.pkl")
 
 # Encodings
-soil_type_encodings = {'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4}
-crop_type_encodings = {'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,
-                       'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,
-                       'Tobacco': 10, 'Wheat': 11}
-fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,
-                             '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,
-                             'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,
-                             'Potassium chloride/Muriate of potash (MOP)': 13, 'Potassium sulfate/Sulfate of potash (SOP)': 14,
-                             'Rock phosphate (RP)': 15, 'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,
-                             'Urea': 18, 'Zinc sulfate': 19}
+soil_type_encodings = {
+    'Black': 0, 'Clayey': 1, 'Loamy': 2, 'Red': 3, 'Sandy': 4
+}
+crop_type_encodings = {
+    'Barley': 0, 'Cotton': 1, 'Ground Nuts': 2, 'Maize': 3, 'Millets': 4,
+    'Oil seeds': 5, 'Other Variety': 6, 'Paddy': 7, 'Pulses': 8, 'Sugarcane': 9,
+    'Tobacco': 10, 'Wheat': 11
+}
+fertilizer_name_encodings = {
+    '10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17-17': 3, '20-20': 4,
+    '20-20-20': 5, '28-28': 6, 'Ammonium sulfate': 7, 'Biofertilizer (e.g., Rhizobium)': 8,
+    'Calcium nitrate': 9, 'DAP': 10, 'Ferrous sulfate': 11, 'Magnesium sulfate': 12,
+    'Potassium chloride/Muriate of potash (MOP)': 13,
+    'Potassium sulfate/Sulfate of potash (SOP)': 14, 'Rock phosphate (RP)': 15,
+    'Single superphosphate (SSP)': 16, 'Triple superphosphate (TSP)': 17,
+    'Urea': 18, 'Zinc sulfate': 19
+}
 inv_fert_name_encodings = {v: k for k, v in fertilizer_name_encodings.items()}
 
-# Gemini AI config
+# Configure Gemini
 genai.configure(api_key=os.getenv("GEMINI_API"))
 model = genai.GenerativeModel("gemini-1.5-flash")
 
@@ -119,11 +125,11 @@ def generate_ai_suggestions(fertilizer_name):
     return response.text.strip()
 
 def predict_fertilizer(temp, humidity, moisture, soil_type, crop_type, nitrogen, potassium, phosphorous):
-    # Encode categorical
+    # Encode categorical values
     soil_encoded = soil_type_encodings.get(soil_type, -1)
     crop_encoded = crop_type_encodings.get(crop_type, -1)
 
-    # Prepare input
+    # Prepare input for model
     input_df = pd.DataFrame([{
         "Temperature": temp,
         "Humidity": humidity,
@@ -135,44 +141,43 @@ def predict_fertilizer(temp, humidity, moisture, soil_type, crop_type, nitrogen,
         "Crop Type": crop_encoded,
     }])
 
-    # Predict fertilizer name
+    # Model predictions
     pred_name_encoded = rf_ferti_name.predict(input_df)[0]
     fert_name = inv_fert_name_encodings[pred_name_encoded]
-
-    # Predict quantity
     fert_qty = rf_ferti_value.predict(input_df)[0]
 
-    # AI suggestions
+    # AI Suggestions
     ai_info = generate_ai_suggestions(fert_name)
 
-    return (
-        f"🌾 Recommended Fertilizer: **{fert_name}**\n"
-        f"📦 Estimated Quantity: **{fert_qty:.2f} units**\n\n"
-        f"📘 AI Suggestions:\n{ai_info}"
+    # Output message
+    result = (
+        f"🌾 **Recommended Fertilizer**: `{fert_name}`\n"
+        f"📦 **Estimated Quantity**: `{fert_qty:.2f} units`\n\n"
+        f"📘 **AI Suggestions**:\n{ai_info}"
     )
+    return result
 
-# Gradio UI
+# Gradio UI components
 soil_list = list(soil_type_encodings.keys())
 crop_list = list(crop_type_encodings.keys())
 
 interface = gr.Interface(
     fn=predict_fertilizer,
     inputs=[
-        gr.Slider(0, 50, label="Temperature (°C)"),
-        gr.Slider(0, 100, label="Humidity (%)"),
-        gr.Slider(0, 100, label="Moisture (%)"),
-        gr.Dropdown(soil_list, label="Soil Type"),
-        gr.Dropdown(crop_list, label="Crop Type"),
-        gr.Number(label="Nitrogen"),
-        gr.Number(label="Potassium"),
-        gr.Number(label="Phosphorous"),
+        gr.Slider(0, 50, label="🌡️ Temperature (°C)"),
+        gr.Slider(0, 100, label="💧 Humidity (%)"),
+        gr.Slider(0, 100, label="🌱 Moisture (%)"),
+        gr.Dropdown(soil_list, label="🧱 Soil Type"),
+        gr.Dropdown(crop_list, label="🌾 Crop Type"),
+        gr.Number(label="🧪 Nitrogen Level"),
+        gr.Number(label="🧪 Potassium Level"),
+        gr.Number(label="🧪 Phosphorous Level")
     ],
-    outputs=gr.Markdown(label="Result"),
+    outputs=gr.Markdown(label="🧾 Result"),
     title="🌿 Fertilizer Recommendation System",
-    description="Enter environmental and soil parameters to get the optimal fertilizer recommendation and AI-generated suggestions.",
+    description="Enter environmental and soil parameters to get fertilizer suggestions powered by AI.",
+    allow_flagging="never"  # Prevent write-permission errors on Hugging Face
 )
 
-# API Launch
 if __name__ == "__main__":
     interface.launch()
-