Fix model loading and prediction logic in various APIs; improve error…

… handling and update dependencies

agro-quiz-app/components.json

@@ -4,14 +4,14 @@
   "rsc": true,
   "tsx": true,
   "tailwind": {
-    "config": "tailwind.config.js",
-    "css": "src/app/globals.css",
+    "config": "./tailwind.config.js",
+    "css": "./src/app/globals.css",
     "baseColor": "slate",
     "cssVariables": true,
     "prefix": ""
   },
   "aliases": {
-    "components": "@/components",
-    "utils": "@/lib/utils"
+    "@components": "./src/components",
+    "@utils": "./src/lib/utils"
   }
-}
+}

agro-quiz-app/next.config.js

@@ -1,4 +1,14 @@
 /** @type {import('next').NextConfig} */
-const nextConfig = {}
-
-module.exports = nextConfig
+const nextConfig = {
+    reactStrictMode: true, // Enables React strict mode
+    swcMinify: true,       // Enables SWC-based minification for faster builds and smaller bundle sizes
+    images: {
+      domains: ['example.com'], // Allow images from specific domains
+    },
+    experimental: {
+      appDir: true, // Enables the new Next.js app directory (for Next.js 13+)
+    },
+  };
+
+  module.exports = nextConfig;
+

agro-quiz-app/package.json

@@ -14,23 +14,23 @@
     "class-variance-authority": "^0.7.0",
     "clsx": "^2.1.0",
     "lucide-react": "^0.303.0",
-    "next": "14.0.4",
-    "react": "^18",
-    "react-dom": "^18",
+    "next": "^14.0.4",
+    "react": "^18.2.0",
+    "react-dom": "^18.2.0",
     "react-icons": "^4.12.0",
     "react-toastify": "^9.1.3",
     "tailwind-merge": "^2.2.0",
     "tailwindcss-animate": "^1.0.7"
   },
   "devDependencies": {
-    "@types/node": "^20",
-    "@types/react": "^18",
-    "@types/react-dom": "^18",
-    "autoprefixer": "^10.0.1",
-    "eslint": "^8",
-    "eslint-config-next": "14.0.4",
-    "postcss": "^8",
+    "@types/node": "^20.0.0",
+    "@types/react": "^18.0.0",
+    "@types/react-dom": "^18.0.0",
+    "autoprefixer": "^10.4.0",
+    "eslint": "^8.50.0",
+    "eslint-config-next": "^14.0.4",
+    "postcss": "^8.4.0",
     "tailwindcss": "^3.3.0",
-    "typescript": "^5"
+    "typescript": "^5.0.0"
   }
 }

agro-quiz-app/postcss.config.js

@@ -1,6 +1,8 @@
 module.exports = {
   plugins: {
+    // Tailwind CSS integration
     tailwindcss: {},
+    // Adds vendor prefixes for cross-browser compatibility
     autoprefixer: {},
   },
-}
+};

agro-quiz-app/tailwind.config.js

@@ -7,7 +7,6 @@ module.exports = {
     './app/**/*.{ts,tsx}',
     './src/**/*.{ts,tsx}',
   ],
-  prefix: "",
   theme: {
     container: {
       center: true,
@@ -74,4 +73,4 @@ module.exports = {
     },
   },
   plugins: [require("tailwindcss-animate")],
-}
+};

agro-quiz-app/tailwind.config.ts

@@ -18,3 +18,4 @@ const config: Config = {
   plugins: [],
 }
 export default config
+

agro-quiz-app/tsconfig.json

@@ -18,8 +18,9 @@
         "name": "next"
       }
     ],
+    "baseUrl": "./",
     "paths": {
-      "@/*": ["./src/*"]
+      "@/*": ["src/*"]
     }
   },
   "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx", ".next/types/**/*.ts"],

agrotech-ai-apis/app.py

@@ -19,37 +19,32 @@
 soil_lab_prompt = (
     "As an expert in location-based services and geospatial data, your task is to provide a precise and accurate list of nearby soil testing labs "
     "for the specified location. Please return the response in a well-structured JSON format. "
-    "Each entry should include the lab's name, latitude, longitude, and a direct Google Maps link for easy navigation. "
+    "Each entry should include the lab's name, latitude, longitude, and a direct Google Maps link for easy navigation."
 )
 
 ee_shop_prompt = (
     "As an expert in location-based services and geospatial data, your task is to provide a precise and accurate list of nearby electrical and electronics shops "
     "for the specified location. Please return the response in a well-structured JSON format. "
-    "Each entry should include the shop's name, latitude, longitude, and a direct Google Maps link for easy navigation. "
+    "Each entry should include the shop's name, latitude, longitude, and a direct Google Maps link for easy navigation."
 )
 
 # Initialize Flask app
 app = Flask(__name__)
 CORS(app)
 
-# Function to load Gemini Pro model and get responses
+# Load Gemini Pro model
 model = genai.GenerativeModel("gemini-pro")
 chat = model.start_chat(history=[])
 
 def extract_json(text):
     json_pattern = r'\{.*\}|\[.*\]'
     match = re.search(json_pattern, text, re.DOTALL)
-    if match:
-        return match.group(0)
-    return None
+    return match.group(0) if match else None
 
 def get_gemini_response(location, prompt):
-    full_prompt = prompt + location
+    full_prompt = f"{prompt} {location}"
     response = chat.send_message(full_prompt, stream=True)
-    response_text = ""
-    for chunk in response:
-        response_text += chunk.text
-    return response_text
+    return ''.join(chunk.text for chunk in response)
 
 # API route to get soil testing labs based on location
 @app.route('/find_soil_labs', methods=['POST'])
@@ -68,8 +63,7 @@ def find_soil_labs():
             return jsonify(soil_labs), 200
         except json.JSONDecodeError:
             return jsonify({"error": "Error decoding the JSON data."}), 500
-    else:
-        return jsonify({"error": "No valid JSON found in the response."}), 500
+    return jsonify({"error": "No valid JSON found in the response."}), 500
 
 # API route to get nearby electrical and electronics shops based on location
 @app.route('/find_ee_shops', methods=['POST'])
@@ -88,8 +82,7 @@ def find_ee_shops():
             return jsonify(ee_shops), 200
         except json.JSONDecodeError:
             return jsonify({"error": "Error decoding the JSON data."}), 500
-    else:
-        return jsonify({"error": "No valid JSON found in the response."}), 500
+    return jsonify({"error": "No valid JSON found in the response."}), 500
 
 # API route to check mushroom edibility
 @app.route("/mushroom_edibility", methods=["POST"])
@@ -98,32 +91,29 @@ def mushroom_edibility():
 
 # API route to recommend crops based on soil and previous crop information
 @app.route('/crop_recommendation', methods=['POST'])
-def crop_recommendation():
+def crop_recommendation_route():
+    data = request.json
+    if not data:
+        return jsonify({'error': 'No data provided'}), 400
+
     try:
-        data = request.json
         print("Received data:", data)  # Debugging: Log received data
-
-        # Call the crop recommendation function
         result = recommend_crop(data)
         return jsonify(result), 200
-
     except Exception as e:
         return jsonify({'error': str(e)}), 500
 
 # API route for seed quality prediction
 @app.route('/predict_seed_quality', methods=['POST'])
 def predict_seed_quality_route():
-    """API endpoint for predicting seed quality."""
     if 'file' not in request.files:
         return jsonify({'error': 'No file provided'}), 400
 
     file = request.files['file']
-
     if not file:
         return jsonify({'error': 'No file provided'}), 400
 
     try:
-        # Call the seed quality prediction function
         result = predict_seed_quality(file)
         return jsonify(result), 200
     except Exception as e:
@@ -136,7 +126,6 @@ def predict():
         return jsonify({"error": "No image part in the request"}), 400
 
     file = request.files['image']
-
     if file.filename == '':
         return jsonify({"error": "No image selected for uploading"}), 400
 
@@ -148,11 +137,7 @@ def predict():
             return jsonify({"error": "Prediction failed"}), 500
 
         class_name = get_class_name(result_index)
-        if class_name:
-            return jsonify({"prediction": class_name}), 200
-        else:
-            return jsonify({"error": "Invalid class index returned."}), 500
-
+        return jsonify({"prediction": class_name}), 200 if class_name else jsonify({"error": "Invalid class index returned."}), 500
     except Exception as e:
         print(f"Error in /predict: {e}")
         return jsonify({"error": "An error occurred during prediction"}), 500

agrotech-ai-apis/crop_recommendation.py

@@ -45,8 +45,8 @@ def recommend_crop(data):
 
         # Prepare data for prediction
         input_data = pd.DataFrame([{
-            "Previous Crop": previous_crop_mapping.get(previous_crop, -1),  # Map to integer
-            "Soil Type": soil_type_mapping.get(soil_type, -1),  # Map to integer
+            "Previous Crop": previous_crop_mapping.get(previous_crop, -1),  # Map to integer or -1 if not found
+            "Soil Type": soil_type_mapping.get(soil_type, -1),  # Map to integer or -1 if not found
             "Moisture Level": moisture_level,
             "Nitrogen (N)": nitrogen,
             "Phosphorus (P)": phosphorus,
@@ -56,10 +56,8 @@ def recommend_crop(data):
         # Make prediction
         prediction = crop_model.predict(input_data)
 
-        if prediction[0] in crop_mapping:
-            recommended_crop = crop_mapping[prediction[0]]
-        else:
-            return {'Recommended Crop': 'No prediction available'}
+        # Map prediction to crop name
+        recommended_crop = crop_mapping.get(prediction[0], 'No prediction available')
 
         return {'Recommended Crop': str(recommended_crop)}
 

agrotech-ai-apis/mushroom_edibility.py

@@ -31,35 +31,37 @@ def check_mushroom_edibility():
         }
 
         data_dict = {}
-
         for key, mapping in mappings.items():
             value = request.form.get(key)
             if value:
                 mapped_value = mapping.get(value)
                 data_dict[key] = [mapped_value if mapped_value is not None else None]
             else:
-                raise ValueError(f"Missing value in column: {key} with value {value}")
                 data_dict[key] = [None]
 
         df = pd.DataFrame(data_dict)
 
+        # Load encoders
         with open('./models/encoders.pkl', 'rb') as f:
             encoders = pickle.load(f)
 
+        # Transform using encoders
         for col in df.columns:
             if col in encoders:
                 if df[col].isnull().any():
                     raise ValueError(f"Missing value in column: {col}")
                 df[col] = encoders[col].transform(df[col])
 
-        return jsonify({"edibility": edibility_check(df)})
+        # Get edibility prediction
+        edibility_result = edibility_check(df)
+        return jsonify({"edibility": edibility_result})
 
     except Exception as e:
         return jsonify({"error": str(e)})
 
-
 def edibility_check(df):
     with open("./models/model.pkl", "rb") as f:
         model = pickle.load(f)
+
     prediction = model.predict(df)
     return "Edible" if prediction[0] == 1 else "Poisonous"

agrotech-ai-apis/paddy_prediction.py

@@ -10,8 +10,10 @@
 model = load_model('models/rice_model.h5', compile=False)
 
 # Class labels for the paddy diseases
-modified_class_label = ['Bacterial Leaf Blight', 'Bacterial Leaf Streak', 'Bacterial Panicle Blight', 'Blast',
- 'Brown Spot', 'Dead Heart', 'Downy Mildew', 'Hispa', 'Normal', 'Tungro']
+modified_class_label = [
+    'Bacterial Leaf Blight', 'Bacterial Leaf Streak', 'Bacterial Panicle Blight', 'Blast',
+    'Brown Spot', 'Dead Heart', 'Downy Mildew', 'Hispa', 'Normal', 'Tungro'
+]
 
 LABEL_DESCRIPTION = [
     {
@@ -87,25 +89,31 @@
 ]
 
 def preprocess_image(image_path, target_size=(256, 256)):
+    """Load and preprocess the image for prediction."""
     img = load_img(image_path, target_size=target_size)
     img_array = img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)
     img_array = tf.cast(img_array / 255.0, tf.float32)
     return img_array
 
-def predict_paddy(filepath):
-    predictions = model.predict(preprocess_image(filepath))
+def predict_paddy(image_path):
+    """Predict the class of the paddy image."""
+    processed_image = preprocess_image(image_path)
+    predictions = model.predict(processed_image)
     predicted_class = np.argmax(predictions, axis=1)
     return predicted_class[0]  # Return the class index
 
 def paddy_prediction(image_path):
+    """Main function to handle the prediction logic."""
     try:
         label_index = predict_paddy(image_path)
         label = modified_class_label[label_index]
+        details = LABEL_DESCRIPTION[label_index]
+
         os.remove(image_path)  # Clean up the image after prediction
         return {
             'prediction': label,
-            'details': LABEL_DESCRIPTION[label_index]
+            'details': details
         }
     except Exception as e:
         return {"error": str(e)}

agrotech-ai-apis/plant_disease_detection.py

@@ -35,8 +35,8 @@ def model_prediction(image_bytes):
         # Load and preprocess the image
         image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
         image = image.resize((224, 224))
-        input_arr = np.array(image)
-        input_arr = np.expand_dims(input_arr, axis=0).astype(np.float32) / 255.0
+        input_arr = np.array(image, dtype=np.float32) / 255.0  # Normalize the image
+        input_arr = np.expand_dims(input_arr, axis=0)  # Add batch dimension
 
         # Set the tensor for the input
         interpreter.set_tensor(input_details[0]['index'], input_arr)
@@ -47,6 +47,7 @@ def model_prediction(image_bytes):
         # Get the output tensor and return the index of the max element
         output_data = interpreter.get_tensor(output_details[0]['index'])
         return int(np.argmax(output_data))
+
     except Exception as e:
         print(f"Error in model_prediction: {e}")
         return None
@@ -55,4 +56,4 @@ def get_class_name(index):
     """Function to get the class name based on the index."""
     if index is not None and 0 <= index < len(CLASS_NAMES):
         return CLASS_NAMES[index]
-    return None
+    return "Unknown Class"  # Improved handling for invalid index