Update app.py

app.py

@@ -1,17 +1,99 @@
-from flask import Flask, render_template, request
-import joblib
+# from flask import Flask, render_template, request
+# import joblib
+# import pandas as pd
+# import google.generativeai as genai
+# import os
+
+
+# app = Flask(__name__)
+
+# # Load the trained Random Forest models
+# rf_ferti_name = joblib.load('rf_ferti_name.pkl')
+# rf_ferti_value = joblib.load('rf_ferti_value.pkl')
+
+# # Manually define the encodings based on the provided dictionaries
+# 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}
+
+
+# # AI configuration
+# api_key=os.getenv('GEMINI_API')
+# genai.configure(api_key=api_key)
+# model = genai.GenerativeModel("gemini-1.5-flash")
+
+# def generate_ai_suggestions(pred_fertilizer_name):
+#     prompt = (
+#         f"For {pred_fertilizer_name} fertlizer, generate 3-4  sentences each on a new line, note text shoudl be jsutidied should not contian anyu special character"
+#     )
+#     response = model.generate_content(prompt)
+#     return response.text
+
+
+
+# @app.route('/', methods=['GET', 'POST'])
+# def index():
+#     if request.method == 'POST':
+#         # Retrieve form data
+#         temperature = float(request.form['temperature'])
+#         humidity = float(request.form['humidity'])
+#         moisture = float(request.form['moisture'])
+#         soil_type = request.form['soil_type']
+#         crop_type = request.form['crop_type']
+#         nitrogen = float(request.form['nitrogen'])
+#         potassium = float(request.form['potassium'])
+#         phosphorous = float(request.form['phosphorous'])
+
+#         # Encode categorical data
+#         soil_type_encoded = soil_type_encodings.get(soil_type, -1)
+#         crop_type_encoded = crop_type_encodings.get(crop_type, -1)
+
+#         # Create a DataFrame for the input
+#         user_input = pd.DataFrame({
+#             'Temperature': [temperature],
+#             'Humidity': [humidity],
+#             'Moisture': [moisture],
+#             'Nitrogen': [nitrogen],
+#             'Potassium': [potassium],
+#             'Phosphorous': [phosphorous],
+#             'Soil Type': [soil_type_encoded],
+#             'Crop Type': [crop_type_encoded]
+#         })
+
+#         # Predict Fertilizer Name
+#         pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]
+#         pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]
+
+#         # Predict Fertilizer Quantity
+#         pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]
+#         pred_info = generate_ai_suggestions(pred_fertilizer_name)
+
+#         return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,
+#                                fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty,pred_info=pred_info)
+#     return render_template('index.html', prediction=False)
+
+# if __name__ == '__main__':
+#     app.run(port=7860,host='0.0.0.0')
+
+
+import gradio as gr
 import pandas as pd
-import google.generativeai as genai
+import joblib
 import os
+import google.generativeai as genai
 
+# Load models
+rf_ferti_name = joblib.load("rf_ferti_name.pkl")
+rf_ferti_value = joblib.load("rf_ferti_value.pkl")
 
-app = Flask(__name__)
-
-# Load the trained Random Forest models
-rf_ferti_name = joblib.load('rf_ferti_name.pkl')
-rf_ferti_value = joblib.load('rf_ferti_value.pkl')
-
-# Manually define the encodings based on the provided dictionaries
+# 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,
@@ -22,62 +104,75 @@ fertilizer_name_encodings = {'10-26-26': 0, '14-35-14': 1, '15-15-15': 2, '17-17
                              '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()}
 
-
-# AI configuration
-api_key=os.getenv('GEMINI_API')
-genai.configure(api_key=api_key)
+# Gemini AI config
+genai.configure(api_key=os.getenv("GEMINI_API"))
 model = genai.GenerativeModel("gemini-1.5-flash")
 
-def generate_ai_suggestions(pred_fertilizer_name):
+def generate_ai_suggestions(fertilizer_name):
     prompt = (
-        f"For {pred_fertilizer_name} fertlizer, generate 3-4  sentences each on a new line, note text shoudl be jsutidied should not contian anyu special character"
+        f"For {fertilizer_name} fertilizer, generate 3–4 short suggestions. "
+        "Each on a new line. Text should be justified and not contain special characters."
     )
     response = model.generate_content(prompt)
-    return response.text
-
-
-
-@app.route('/', methods=['GET', 'POST'])
-def index():
-    if request.method == 'POST':
-        # Retrieve form data
-        temperature = float(request.form['temperature'])
-        humidity = float(request.form['humidity'])
-        moisture = float(request.form['moisture'])
-        soil_type = request.form['soil_type']
-        crop_type = request.form['crop_type']
-        nitrogen = float(request.form['nitrogen'])
-        potassium = float(request.form['potassium'])
-        phosphorous = float(request.form['phosphorous'])
-
-        # Encode categorical data
-        soil_type_encoded = soil_type_encodings.get(soil_type, -1)
-        crop_type_encoded = crop_type_encodings.get(crop_type, -1)
-
-        # Create a DataFrame for the input
-        user_input = pd.DataFrame({
-            'Temperature': [temperature],
-            'Humidity': [humidity],
-            'Moisture': [moisture],
-            'Nitrogen': [nitrogen],
-            'Potassium': [potassium],
-            'Phosphorous': [phosphorous],
-            'Soil Type': [soil_type_encoded],
-            'Crop Type': [crop_type_encoded]
-        })
-
-        # Predict Fertilizer Name
-        pred_fertilizer_name = rf_ferti_name.predict(user_input)[0]
-        pred_fertilizer_name = [name for name, value in fertilizer_name_encodings.items() if value == pred_fertilizer_name][0]
-
-        # Predict Fertilizer Quantity
-        pred_fertilizer_qty = rf_ferti_value.predict(user_input)[0]
-        pred_info = generate_ai_suggestions(pred_fertilizer_name)
-
-        return render_template('index.html', prediction=True, fertilizer_name=pred_fertilizer_name,
-                               fertilizer_qty=pred_fertilizer_qty, optimal_usage=pred_fertilizer_qty,pred_info=pred_info)
-    return render_template('index.html', prediction=False)
-
-if __name__ == '__main__':
-    app.run(port=7860,host='0.0.0.0')
+    return response.text.strip()
+
+def predict_fertilizer(temp, humidity, moisture, soil_type, crop_type, nitrogen, potassium, phosphorous):
+    # Encode categorical
+    soil_encoded = soil_type_encodings.get(soil_type, -1)
+    crop_encoded = crop_type_encodings.get(crop_type, -1)
+
+    # Prepare input
+    input_df = pd.DataFrame([{
+        "Temperature": temp,
+        "Humidity": humidity,
+        "Moisture": moisture,
+        "Nitrogen": nitrogen,
+        "Potassium": potassium,
+        "Phosphorous": phosphorous,
+        "Soil Type": soil_encoded,
+        "Crop Type": crop_encoded,
+    }])
+
+    # Predict fertilizer name
+    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_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}"
+    )
+
+# Gradio UI
+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"),
+    ],
+    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.",
+)
+
+# API Launch
+if __name__ == "__main__":
+    interface.launch()
+