Changes paths for file import

SVM/SVM_C.py

@@ -1,125 +1,125 @@
-import os
-import pickle
-import pandas as pd
-
-class SVM_Classifier:
-    def __init__(self):
-        
-        self.weight = 70  # default weight in kg
-        self.height = 1.75  # default height in m
-        self.gender = "Male"  # default gender
-        self.duration = 1.0  # default duration in hours
-        self.fat = 25  # default fat percentage
-        self.freq = 3  # default workouts per week
-        self.experience = 1  # default experience level
-        self.workout = "Cardio"  # default workout type
-
-        # Add debug info dictionary
-        self.debug_info = {}
-        
-        # Load the model and required data
-        try:
-            # Load the SVM model
-            model_file = os.path.join("app", "data", "svm_model.pkl")
-            with open(model_file, 'rb') as f:
-                self.svm_model = pickle.load(f)
-            
-            # Load the column names
-            cols_file = os.path.join("app", "data", "column_names.csv")
-            with open(cols_file, 'r') as f:
-                self.column_names = [line.strip() for line in f]
-            
-            # Load normalization parameters
-            mean_file = os.path.join("app", "data", "SVM_train_mean.csv")
-            self.df_mean = pd.read_csv(mean_file, index_col=0)
-            
-            std_file = os.path.join("app", "data", "SVM_train_std.csv")
-            self.df_std = pd.read_csv(std_file, index_col=0)
-            
-        except Exception as e:
-            print(f"Error loading model files: {str(e)}")
-            raise
-
-    def make_prediction(self):
-        try:
-            num = [self.weight, self.height, self.duration, self.fat, self.freq, self.experience]
-            self.debug_info['original_values'] = dict(zip(
-                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'], 
-                num
-            ))
-
-            m_norm = self.df_mean.values.flatten().tolist()
-            s_norm = self.df_std.values.flatten().tolist()
-            self.debug_info['normalization'] = {
-                'means': m_norm,
-                'stds': s_norm
-            }
-
-            norm = [(x-y)/z for x, y, z in zip(num, m_norm, s_norm)]
-            self.debug_info['normalized_values'] = dict(zip(
-                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'],
-                norm
-            ))
-
-            if self.gender == 'Female':
-                norm.extend([1,0])
-                self.debug_info['gender_encoding'] = 'Female: [1, 0]'
-            else:
-                norm.extend([0,1])
-                self.debug_info['gender_encoding'] = 'Male: [0, 1]'
-
-            # Add one-hot encoded workout type
-            workout_encoding = {
-                'Cardio': [1, 0, 0, 0],
-                'HIIT': [0, 1, 0, 0],
-                'Strength': [0, 0, 1, 0],
-                'Yoga': [0, 0, 0, 1]
-            }
-            norm.extend(workout_encoding[self.workout])
-            self.debug_info['workout_encoding'] = f'{self.workout}: {workout_encoding[self.workout]}'
-
-            X = pd.DataFrame([norm],columns=self.column_names)
-            self.debug_info['final_feature_vector'] = X.to_dict('records')[0]
-
-            prediction = self.svm_model.predict(X)
-            self.debug_info['prediction'] = prediction[0]
-
-            return prediction[0]
-
-        except Exception as e:
-            self.debug_info['error'] = str(e)
-            return f"Error: {str(e)}"
-
-    def get_debug_info(self):
-        """Returns formatted debug information"""
-        debug_text = "=== DEBUG INFORMATION ===\n\n"
-        
-        # Original values
-        debug_text += "Original Values:\n"
-        for key, value in self.debug_info['original_values'].items():
-            debug_text += f"{key}: {value}\n"
-        
-        # Normalization parameters
-        debug_text += "\nNormalization Parameters:\n"
-        for i, (mean, std) in enumerate(zip(
-            self.debug_info['normalization']['means'],
-            self.debug_info['normalization']['stds']
-        )):
-            debug_text += f"Feature {i}: mean={mean:.4f}, std={std:.4f}\n"
-        
-        # Normalized values
-        debug_text += "\nNormalized Values:\n"
-        for key, value in self.debug_info['normalized_values'].items():
-            debug_text += f"{key}: {value:.4f}\n"
-        
-        # Encodings
-        debug_text += f"\nGender Encoding: {self.debug_info['gender_encoding']}\n"
-        debug_text += f"Workout Encoding: {self.debug_info['workout_encoding']}\n"
-
-        # Final vector
-        debug_text += f"\nVector: {self.debug_info['final_feature_vector']}\n"
-        
-        # Final prediction
-        debug_text += f"\nFinal Prediction: {self.debug_info['prediction']}\n"
-        
-        return debug_text
+import os
+import pickle
+import pandas as pd
+
+class SVM_Classifier:
+    def __init__(self):
+        
+        self.weight = 70  # default weight in kg
+        self.height = 1.75  # default height in m
+        self.gender = "Male"  # default gender
+        self.duration = 1.0  # default duration in hours
+        self.fat = 25  # default fat percentage
+        self.freq = 3  # default workouts per week
+        self.experience = 1  # default experience level
+        self.workout = "Cardio"  # default workout type
+
+        # Add debug info dictionary
+        self.debug_info = {}
+        
+        # Load the model and required data
+        try:
+            # Load the SVM model
+            model_file = os.path.join("data", "svm_model.pkl")
+            with open(model_file, 'rb') as f:
+                self.svm_model = pickle.load(f)
+            
+            # Load the column names
+            cols_file = os.path.join("data", "column_names.csv")
+            with open(cols_file, 'r') as f:
+                self.column_names = [line.strip() for line in f]
+            
+            # Load normalization parameters
+            mean_file = os.path.join("data", "SVM_train_mean.csv")
+            self.df_mean = pd.read_csv(mean_file, index_col=0)
+            
+            std_file = os.path.join("data", "SVM_train_std.csv")
+            self.df_std = pd.read_csv(std_file, index_col=0)
+            
+        except Exception as e:
+            print(f"Error loading model files: {str(e)}")
+            raise
+
+    def make_prediction(self):
+        try:
+            num = [self.weight, self.height, self.duration, self.fat, self.freq, self.experience]
+            self.debug_info['original_values'] = dict(zip(
+                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'], 
+                num
+            ))
+
+            m_norm = self.df_mean.values.flatten().tolist()
+            s_norm = self.df_std.values.flatten().tolist()
+            self.debug_info['normalization'] = {
+                'means': m_norm,
+                'stds': s_norm
+            }
+
+            norm = [(x-y)/z for x, y, z in zip(num, m_norm, s_norm)]
+            self.debug_info['normalized_values'] = dict(zip(
+                ['weight', 'height', 'duration', 'fat', 'freq', 'experience'],
+                norm
+            ))
+
+            if self.gender == 'Female':
+                norm.extend([1,0])
+                self.debug_info['gender_encoding'] = 'Female: [1, 0]'
+            else:
+                norm.extend([0,1])
+                self.debug_info['gender_encoding'] = 'Male: [0, 1]'
+
+            # Add one-hot encoded workout type
+            workout_encoding = {
+                'Cardio': [1, 0, 0, 0],
+                'HIIT': [0, 1, 0, 0],
+                'Strength': [0, 0, 1, 0],
+                'Yoga': [0, 0, 0, 1]
+            }
+            norm.extend(workout_encoding[self.workout])
+            self.debug_info['workout_encoding'] = f'{self.workout}: {workout_encoding[self.workout]}'
+
+            X = pd.DataFrame([norm],columns=self.column_names)
+            self.debug_info['final_feature_vector'] = X.to_dict('records')[0]
+
+            prediction = self.svm_model.predict(X)
+            self.debug_info['prediction'] = prediction[0]
+
+            return prediction[0]
+
+        except Exception as e:
+            self.debug_info['error'] = str(e)
+            return f"Error: {str(e)}"
+
+    def get_debug_info(self):
+        """Returns formatted debug information"""
+        debug_text = "=== DEBUG INFORMATION ===\n\n"
+        
+        # Original values
+        debug_text += "Original Values:\n"
+        for key, value in self.debug_info['original_values'].items():
+            debug_text += f"{key}: {value}\n"
+        
+        # Normalization parameters
+        debug_text += "\nNormalization Parameters:\n"
+        for i, (mean, std) in enumerate(zip(
+            self.debug_info['normalization']['means'],
+            self.debug_info['normalization']['stds']
+        )):
+            debug_text += f"Feature {i}: mean={mean:.4f}, std={std:.4f}\n"
+        
+        # Normalized values
+        debug_text += "\nNormalized Values:\n"
+        for key, value in self.debug_info['normalized_values'].items():
+            debug_text += f"{key}: {value:.4f}\n"
+        
+        # Encodings
+        debug_text += f"\nGender Encoding: {self.debug_info['gender_encoding']}\n"
+        debug_text += f"Workout Encoding: {self.debug_info['workout_encoding']}\n"
+
+        # Final vector
+        debug_text += f"\nVector: {self.debug_info['final_feature_vector']}\n"
+        
+        # Final prediction
+        debug_text += f"\nFinal Prediction: {self.debug_info['prediction']}\n"
+        
+        return debug_text