image classifier complete

app.py

@@ -1,5 +1,6 @@
 from config import crop_model, crop_pipeline_encoder, crop_label_encoder
 from config import fertilizer_model, fertilizer_pipeline_encoder, fertilizer_label_encoder
+from config import plant_diseases_classifier_model
 from utils import retrieve_image_by_name_from_mongodb, retrieve_data
 from flask import Flask, request, render_template, jsonify
 import requests
@@ -9,6 +10,8 @@ import base64
 
 app = Flask(__name__)
 
+app.config['UPLOAD_FOLDER'] = 'static/uploaded_image'
+
 @app.route("/")
 @app.route("/home")
 def home():
@@ -88,10 +91,36 @@ def fertilizer_recommendation_output():
     return render_template('fertilizer_recommendation_ouput.html', image_data_base64=image_data_base64, label= label[0], fertilizer_details=fertilizer_details)
 
 
-@app.route('/image_classification')
+@app.route('/image_classification', methods=['GET', 'POST'])
 def image_classification():
     return render_template('image_classification_input.html')
 
+@app.route('/image_classification_output', methods=['GET', 'POST'])
+def image_classification_output():
+    file = request.files['image_file']
+    new_filename = "plant_image.JPG"
+    file.save(os.path.join(app.config['UPLOAD_FOLDER'], new_filename))
+    file_path = os.path.join(app.config['UPLOAD_FOLDER'], new_filename)
+
+    # infercing the with the uploaded image
+    results = plant_diseases_classifier_model(file_path)
+    
+    #fetching all the labels 
+    names_dict = results[0].names
+
+    # fetching the probalility of each class
+    probs = results[0].probs.data.tolist()
+
+    # selecting class with maximum probability
+    model_prediction= names_dict[np.argmax(probs)]
+
+    diseases_details = retrieve_data(database_name=os.getenv("DISEASE_DB_NAME"), 
+                                    collection_name=os.getenv("DISEASE_INFO_COLLECTION_NAME"),
+                                    search_query=model_prediction)
+
+    return render_template("image_classification_output.html", model_prediction=model_prediction, diseases_details=diseases_details)
+
+
 @app.route('/market_price')
 def market_price():
     return render_template("market_price_input.html")
@@ -100,7 +129,7 @@ def market_price():
 def market_price_output():
     # input field name is 'selected_state'
     user_input = request.form.get('selected_state')
-    api_key = "579b464db66ec23bdd000001cdd3946e44ce4aad7209ff7b23ac571b"
+    api_key = os.getenv("COMMODITY_PRICE_API_KEY")
 
     # Make a request to the API with the user input
     api_url = f'https://api.data.gov.in/resource/9ef84268-d588-465a-a308-a864a43d0070?api-key={api_key}&format=json&filters%5Bstate%5D={user_input}'
@@ -109,8 +138,12 @@ def market_price_output():
     if response.status_code == 200:
         data = response.json()
         data = data['records']
+        # return render_template('market_price_output.html', data=data)
+        if len(data) > 0:
         # Return the JSON data as a response
-        return render_template('market_price_output.html', data=data)
+            return render_template('market_price_output.html', data=data)
+        else:
+            return render_template("market_price_no_data.html")
     else:
         return jsonify({'error': 'Unable to fetch data from the API'}), 400
 

artifacts.py

@@ -7,6 +7,8 @@ TRANSFORMER_OJBCET_NAME = "transformer.pkl"
 crop_recommendation_artifacts_path = "./crop-recommendation/saved_models"
 fertilizer_recommendation_artifacts_path = "./Fertilizer-Recommendation/saved_models"
 
+plant_diseases_classifier_model_path = "./plant-diseases-classifier/custom_model_weights/best.pt"
+
 
 ## crop recommendation artifacts
 latest_crop_recommendation_artifacts = max(os.listdir(crop_recommendation_artifacts_path)) #0, 1, 2

config.py

@@ -1,7 +1,9 @@
 from artifacts import crop_model_path, crop_transformer_path, crop_target_encoder_path
 from artifacts import fertilizer_model_path, fertilizer_transformer_path, fertilizer_target_encoder_path
+from artifacts import plant_diseases_classifier_model_path
 
 from utils import load_model_and_encoders
+from ultralytics import YOLO
 
 crop_model, crop_pipeline_encoder, crop_label_encoder = load_model_and_encoders(model_path=crop_model_path, 
                                                                                 transformer_path=crop_transformer_path,
@@ -9,4 +11,6 @@ crop_model, crop_pipeline_encoder, crop_label_encoder = load_model_and_encoders(
 
 fertilizer_model, fertilizer_pipeline_encoder, fertilizer_label_encoder = load_model_and_encoders(model_path=fertilizer_model_path, 
                                                                                 transformer_path=fertilizer_transformer_path,
-                                                                                target_encoder_path=fertilizer_target_encoder_path)
+                                                                                target_encoder_path=fertilizer_target_encoder_path)
+
+plant_diseases_classifier_model = YOLO(plant_diseases_classifier_model_path)

requirements.txt

@@ -4,4 +4,5 @@ requests
 numpy
 python-dotenv
 scikit-learn
-dill
+dill
+ultralytics

templates/index.html

@@ -229,8 +229,7 @@
           <div class="card-body">
             <h5 class="card-title">Crop Recommendation</h5>
             <p class="card-text">
-              Some quick example text to build on the card title and make up the
-              bulk of the card's content.
+              <b>Harvesting Insights:</b> Transforming Farming with AI-Driven Crop Recommendations
             </p>
             <a href="{{url_for('crop_recommendation')}}" class="btn btn-primary"
               >Visit Page</a
@@ -248,8 +247,7 @@
           <div class="card-body">
             <h5 class="card-title">Fertilizer Recommendation</h5>
             <p class="card-text">
-              Some quick example text to build on the card title and make up the
-              bulk of the card's content.
+              <b>Fertile Future:</b> Enhancing Agriculture with AI-Driven Fertilizer Recommendations
             </p>
             <a
               href="{{url_for('fertilizer_recommendation')}}"
@@ -269,8 +267,7 @@
           <div class="card-body">
             <h5 class="card-title">Dieases Classification</h5>
             <p class="card-text">
-              Some quick example text to build on the card title and make up the
-              bulk of the card's content.
+              <b>Sight to Insight:</b> Advancing Farming through Image-Based Disease Identification
             </p>
             <a
               href="{{url_for('image_classification')}}"
@@ -290,8 +287,7 @@
           <div class="card-body">
             <h5 class="card-title">Market Price</h5>
             <p class="card-text">
-              Some quick example text to build on the card title and make up the
-              bulk of the card's content.
+             <b>Price Insights:</b> Commodity Markets in Indian States with Real-time Government Data
             </p>
             <a href="{{url_for('market_price')}}" class="btn btn-primary"
               >Visit Page</a
@@ -307,8 +303,8 @@
   <div class="container text-center">
     <p>Connect on Social Media</p>
     <div class="social-icons">
-      <a href="#"><img src="/static/images/github-logo.png" alt="" /></a>
-      <a href="#"><img src="/static/images/linkedin-icon.png" alt="" /></a>
+      <a href="https://github.com/07Sada"><img src="/static/images/github-logo.png" alt="" /></a>
+      <a href="https://www.linkedin.com/in/sadashiv-nandanikar/"><img src="/static/images/linkedin-icon.png" alt="" /></a>
     </div>
   </div>
 </section>
@@ -320,7 +316,7 @@
       <div class="col-md-4 footer-box">
         <img src="/static/images/logo.png" alt="" />
         <p class="addition-info">
-          Please Try all the features and comment and share
+          I wholeheartedly encourage you to explore all the features and kindly request your valuable feedback. Your insights are highly valued.
         </p>
       </div>
     </div>