File size: 7,150 Bytes
e0c0586 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import pandas as pd
import os
from typing import Dict, List, Any, Optional
import math
class FeatherManager:
def __init__(self, models_dir: str = "models"):
self.models_dir = models_dir
os.makedirs(models_dir, exist_ok=True)
def save_mini_model(self, model_data: Dict[str, Any], model_id: int) -> str:
filename = f"AgGPT_Expert_{model_id:04d}.feather"
filepath = os.path.join(self.models_dir, filename)
patterns = model_data.get('patterns', [])
responses = model_data.get('responses', [])
if not patterns or not responses:
print(f"Warning: Model {model_id} has empty patterns or responses")
patterns = patterns or ['hello']
responses = responses or ['Hello!']
df_data = {
'patterns': [str(pattern) for pattern in patterns],
'responses': [str(response) for response in responses],
'weights': model_data.get('weights', [1.0] * len(patterns)),
'confidence': [model_data.get('confidence', 0.5)] * len(patterns),
'grammar_rules': [str(rule) for rule in model_data.get('grammar_rules', [])] or ['none'],
'keywords': [' '.join(model_data.get('keywords', []))] * len(patterns),
'training_samples': [model_data.get('training_samples', 0)] * len(patterns)
}
max_len = max(len(v) if isinstance(v, list) else 1 for v in df_data.values())
for key, value in df_data.items():
if isinstance(value, list):
while len(value) < max_len:
value.append(value[-1] if value else '')
df = pd.DataFrame(df_data)
df.to_feather(filepath)
print(f"Saved mini-model: {filename}")
return filepath
def load_mini_model(self, model_id: int) -> Optional[Dict[str, Any]]:
filename = f"AgGPT_Expert_{model_id:04d}.feather"
filepath = os.path.join(self.models_dir, filename)
if not os.path.exists(filepath):
return None
try:
df = pd.read_feather(filepath)
model_data = {
'patterns': [p for p in df['patterns'].tolist() if p],
'responses': [r for r in df['responses'].tolist() if r],
'weights': df['weights'].tolist(),
'confidence': df['confidence'].iloc[0] if len(df) > 0 else 0.5,
'grammar_rules': [rule for rule in df['grammar_rules'].tolist() if rule],
'keywords': df['keywords'].iloc[0].split() if len(df) > 0 and df['keywords'].iloc[0] else [],
'training_samples': df['training_samples'].iloc[0] if len(df) > 0 else 0,
'model_id': model_id
}
return model_data
except Exception as e:
print(f"Error loading model {model_id}: {e}")
return None
def load_all_models(self) -> List[Dict[str, Any]]:
models = []
if not os.path.exists(self.models_dir):
return models
for filename in os.listdir(self.models_dir):
if filename.startswith("AgGPT_Expert_") and filename.endswith(".feather"):
try:
model_id = int(filename.split("_")[2].split(".")[0])
model = self.load_mini_model(model_id)
if model:
models.append(model)
except (ValueError, IndexError):
print(f"Warning: Invalid model filename format: {filename}")
continue
return models
def get_model_count(self) -> int:
if not os.path.exists(self.models_dir):
return 0
count = 0
for filename in os.listdir(self.models_dir):
if filename.startswith("AgGPT_Expert_") and filename.endswith(".feather"):
count += 1
return count
def get_next_model_id(self) -> int:
if not os.path.exists(self.models_dir):
return 1
max_id = 0
for filename in os.listdir(self.models_dir):
if filename.startswith("AgGPT_Expert_") and filename.endswith(".feather"):
try:
model_id = int(filename.split("_")[2].split(".")[0])
max_id = max(max_id, model_id)
except (ValueError, IndexError):
continue
return max_id + 1
def delete_model(self, model_id: int) -> bool:
filename = f"AgGPT_Expert_{model_id:04d}.feather"
filepath = os.path.join(self.models_dir, filename)
if os.path.exists(filepath):
try:
os.remove(filepath)
print(f"Deleted model: {filename}")
return True
except Exception as e:
print(f"Error deleting model {model_id}: {e}")
return False
return False
def clear_all_models(self) -> int:
if not os.path.exists(self.models_dir):
return 0
deleted_count = 0
for filename in os.listdir(self.models_dir):
if filename.startswith("AgGPT_Expert_") and filename.endswith(".feather"):
try:
os.remove(os.path.join(self.models_dir, filename))
deleted_count += 1
except Exception as e:
print(f"Error deleting {filename}: {e}")
print(f"Deleted {deleted_count} model files")
return deleted_count
def similarity_score(text1: str, text2: str) -> float:
if not text1 or not text2:
return 0.0
words1 = set(text1.lower().split())
words2 = set(text2.lower().split())
if not words1 or not words2:
return 0.0
intersection = len(words1.intersection(words2))
union = len(words1.union(words2))
return intersection / union if union > 0 else 0.0
def calculate_confidence_score(patterns: List[str], responses: List[str]) -> float:
if not patterns or not responses or len(patterns) != len(responses):
return 0.1
base_confidence = min(0.9, len(patterns) / 10.0)
return max(0.1, min(1.0, base_confidence))
if __name__ == "__main__":
manager = FeatherManager()
test_model = {
'patterns': ['hello', 'hi', 'hey'],
'responses': ['Hello! How can I help you?', 'Hi there!', 'Hey! What\'s up?'],
'weights': [1.0, 0.9, 0.8],
'confidence': 0.8,
'grammar_rules': ['capitalize_first_word', 'end_with_punctuation'],
'keywords': ['greeting', 'hello', 'hi'],
'training_samples': 150
}
model_id = manager.get_next_model_id()
manager.save_mini_model(test_model, model_id)
loaded_model = manager.load_mini_model(model_id)
print(f"Original model: {test_model}")
print(f"Loaded model: {loaded_model}")
print(f"Models count: {manager.get_model_count()}")
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