crop recommendation complete

app.py

@@ -1,5 +1,10 @@
+from config import crop_model, crop_pipeline_encoder, crop_label_encoder
+from utils import retrieve_image_by_name_from_mongodb, retrieve_data
 from flask import Flask, request, render_template, jsonify
 import requests
+import os
+import numpy as np
+import base64
 
 app = Flask(__name__)
 
@@ -8,10 +13,42 @@ app = Flask(__name__)
 def home():
     return render_template('index.html')
 
-@app.route('/crop_recommendation')
+@app.route('/crop_recommendation', methods=['GET', 'POST'])
 def crop_recommendation():
     return render_template('crop_recommendation_input.html')
 
+@app.route("/crop_recommendation_output", methods=['GET', 'POST'])
+def crop_recommendation_output():
+    temperature = request.form.get("temperature")
+    humidity = request.form.get("humidity")
+    ph = request.form.get("ph")
+    nitrogen = request.form.get("nitrogen")
+    potassium = request.form.get("potassium")
+    phosphorous = request.form.get("phosphorous")
+    rain_fall = request.form.get("rain_fall")
+
+    input_list = [nitrogen, phosphorous, potassium, temperature, humidity, ph, rain_fall]
+    input_array = np.array(input_list).reshape(-1, 7).astype(int)
+
+    transformed_data = crop_pipeline_encoder.transform(input_array)
+    model_prediction = crop_model.predict(transformed_data).astype(int)
+
+    label = crop_label_encoder.inverse_transform(model_prediction)
+    
+    # retrieving the image from mongodb dabase
+    image_data = retrieve_image_by_name_from_mongodb(database_name=os.getenv("CROP_DB_NAME"),
+                                                        collection_name=os.getenv("CROP_IMAGE_COLLECTION_NAME"),
+                                                        file_name=str(label[0]))
+
+    # encoding the byte data recieved from the mongodb
+    image_data_base64 = base64.b64encode(image_data).decode('utf-8')
+
+    # retrieving text data from mongodb 
+    crop_details = retrieve_data(database_name=os.getenv("CROP_DB_NAME"), collection_name= os.getenv("CROP_INFO_COLLECTION_NAME"), search_query=label[0])
+
+    return render_template('crop_recommendation_output.html', image_data_base64=image_data_base64, input_file_name=label[0], crop_details=crop_details)
+
+
 @app.route('/fertilizer_recommendation')
 def fertilizer_recommendation():
     return render_template('fertilizer_recommendation_input.html')

artifacts.py

@@ -0,0 +1,29 @@
+import os
+
+MODEL_NAME = "model.pkl" 
+TARGET_ENCODER_OBJECT_NAME = "target_encoder.pkl"
+TRANSFORMER_OJBCET_NAME = "transformer.pkl"
+
+crop_recommendation_artifacts_path = "./crop-recommendation/saved_models"
+fertilizer_recommendation_artifacts_path = "./Fertilizer-Recommendation/saved_models"
+
+
+## crop recommendation artifacts
+latest_crop_recommendation_artifacts = max(os.listdir(crop_recommendation_artifacts_path)) #0, 1, 2
+
+latest_crop_recommendation_artifacts_path = os.path.join(crop_recommendation_artifacts_path, latest_crop_recommendation_artifacts)
+
+crop_model_path = os.path.join(latest_crop_recommendation_artifacts_path, 'model', MODEL_NAME)
+crop_transformer_path = os.path.join(latest_crop_recommendation_artifacts_path,'transformer', TRANSFORMER_OJBCET_NAME)
+crop_target_encoder_path = os.path.join(latest_crop_recommendation_artifacts_path, 'target_encoder', TARGET_ENCODER_OBJECT_NAME)
+
+
+## fertilizer recommendation artifacts
+latest_fertilizer_recommendation_artifacts = max(os.listdir(fertilizer_recommendation_artifacts_path)) #0, 1, 2
+
+latest_fertilizer_recommendation_artifacts_path = os.path.join(fertilizer_recommendation_artifacts_path, latest_fertilizer_recommendation_artifacts)
+
+fertilizer_model_path = os.path.join(latest_fertilizer_recommendation_artifacts_path, 'model', MODEL_NAME)
+fertilizer_transformer_path = os.path.join(latest_fertilizer_recommendation_artifacts_path,'transformer', TRANSFORMER_OJBCET_NAME)
+fertilizer_target_encoder_path = os.path.join(latest_fertilizer_recommendation_artifacts_path, 'target_encoder', TARGET_ENCODER_OBJECT_NAME)
+

config.py

@@ -0,0 +1,6 @@
+from artifacts import crop_model_path, crop_transformer_path, crop_target_encoder_path
+from utils import load_model_and_encoders
+
+crop_model, crop_pipeline_encoder, crop_label_encoder = load_model_and_encoders(model_path=crop_model_path, 
+                                                                                transformer_path=crop_transformer_path,
+                                                                                target_encoder_path=crop_target_encoder_path)

main.py

@@ -1,25 +0,0 @@
-import pymongo
-
-# Provide the mongodb localhost url to connect python to mongodb.
-client = pymongo.MongoClient("mongodb://localhost:27017/neurolabDB")
-
-# Database Name
-dataBase = client["neurolabDB"]
-
-# Collection  Name
-collection = dataBase['Products']
-
-# Sample data
-d = {'companyName': 'iNeuron',
-     'product': 'Affordable AI',
-     'courseOffered': 'Machine Learning with Deployment'}
-
-# Insert above records in the collection
-rec = collection.insert_one(d)
-
-# Lets Verify all the record at once present in the record with all the fields
-all_record = collection.find()
-
-# Printing all records present in the collection
-for idx, record in enumerate(all_record):
-     print(f"{idx}: {record}")

requirements.txt

@@ -1,4 +1,7 @@
 pymongo
 flask
 requests
-numpy
+numpy
+python-dotenv
+scikit-learn
+dill

templates/crop_recommendation_input.html

@@ -8,7 +8,7 @@
 <div
   class="container recommendation-input-container d-flex justify-content-center"
 >
-  <form class="row g-3" method="GET" action="/crop_recommendation_output">
+  <form class="row g-3" method="POST" action="/crop_recommendation_output">
     <h3 class="text-center">AI-Powered Crop Recommendations</h3>
     <div class="col-md-4" name="temperature">
       <label for="temperature" class="form-label">Temperature</label>

utils.py

@@ -53,4 +53,4 @@ def retrieve_data(database_name, collection_name, search_query):
     result = collection.find_one(search_query)
 
     client.close()
-    return result['crop_info']
+    return result['data_info']