File size: 7,881 Bytes
82dccf5 | 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 | """
generative_ai.py - Generative AI Module
Supports OpenAI GPT, Google Gemini, Anthropic Claude, and Smart AI fallback
"""
import warnings
warnings.filterwarnings("ignore")
OPENAI_OK = False
GOOGLE_OK = False
ANTHROPIC_OK = False
try:
import openai
OPENAI_OK = True
except ImportError:
pass
try:
import google.generativeai as genai
GOOGLE_OK = True
except ImportError:
pass
try:
import anthropic
ANTHROPIC_OK = True
except ImportError:
pass
def _smart_respond(prompt: str, history: list) -> str:
"""Instant smart AI response without API calls - keyword-based fallback."""
p = prompt.lower()
if any(w in p for w in ["hello", "hi", "hey", "greetings"]):
return "Hello! I'm your AI assistant. How can I help you today?"
if "machine learning" in p or " ml " in p or "machine learning" in p:
return (
"**Machine Learning** enables systems to learn from data without explicit programming. "
"Types: Supervised, Unsupervised, Reinforcement Learning. "
"Popular libraries: scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow."
)
if "deep learning" in p or "neural network" in p or "cnn" in p:
return (
"**Deep Learning** uses multi-layer neural networks to learn complex patterns. "
"Best for: images (CNNs), sequences (RNNs/LSTMs), Transformers. "
"Frameworks: PyTorch, TensorFlow/Keras."
)
if "xgboost" in p or "gradient boosting" in p:
return (
"**XGBoost** builds trees sequentially, each correcting prior errors. "
"Key parameters: n_estimators, max_depth, learning_rate, subsample. "
"Extremely fast and accurate for tabular data."
)
if "lightgbm" in p:
return (
"**LightGBM** uses histogram-based gradient boosting for speed. "
"Great for large datasets. Uses leaf-wise tree growth vs level-wise."
)
if "overfitting" in p or "underfitting" in p:
return (
"**Overfitting** = model memorizes training noise, fails on new data. "
"Fixes: cross-validation, regularization (L1/L2), dropout, more data, simpler model. "
"**Underfitting** = model too simple to capture patterns. Fixes: more features, complex model."
)
if "python" in p:
return (
"**Python** dominates AI/ML thanks to: NumPy, Pandas, scikit-learn, "
"PyTorch, TensorFlow, HuggingFace Transformers. "
"Use virtual environments (venv/conda) to manage dependencies."
)
if "nlp" in p or "natural language" in p or "text" in p:
return (
"**NLP** (Natural Language Processing) enables machines to understand text. "
"Key tasks: sentiment analysis, NER, classification, summarization, translation. "
"Modern approach: HuggingFace Transformers (BERT, GPT, T5), spaCy."
)
if "data" in p and ("clean" in p or "preprocess" in p):
return (
"**Data Preprocessing** steps: 1) Handle missing values (mean/median/mode), "
"2) Encode categoricals (LabelEncoder, OneHot), 3) Scale numeric features, "
"4) Remove outliers, 5) Feature engineering."
)
if "random forest" in p or "rf " in p:
return (
"**Random Forest** is an ensemble of decision trees. "
"Uses bagging and random feature selection. "
"Key params: n_estimators, max_depth, min_samples_split. "
"Good for feature importance and handling missing values."
)
if "classification" in p:
return (
"**Classification** predicts categorical labels. "
"Algorithms: Logistic Regression, Decision Trees, Random Forest, SVM, XGBoost. "
"Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC."
)
if "regression" in p:
return (
"**Regression** predicts continuous values. "
"Algorithms: Linear Regression, Ridge, Lasso, Random Forest, XGBoost. "
"Metrics: MSE, RMSE, MAE, R² Score."
)
if "api" in p or "key" in p or "openai" in p or "gpt" in p:
return (
"To use GPT models, set OPENAI_API_KEY environment variable or pass api_key parameter. "
"Get your key from https://platform.openai.com/api-keys"
)
if "help" in p or "what can you do" in p:
return (
"I can help with: Machine Learning, Deep Learning, NLP, Data Science, "
"Python programming, XGBoost, scikit-learn, TensorFlow, PyTorch, "
"model evaluation, and more! Ask me anything."
)
return (
f"I understand you're asking about: '{prompt[:50]}...'. "
"Try asking about: machine learning, neural networks, XGBoost, Python, "
"NLP, data preprocessing, classification, regression, or specific algorithms!"
)
class GenerativeAI:
def __init__(self, api_key: str = "", provider: str = "smart"):
self.api_key = api_key
self.provider = provider
self._provider = provider
self._provider_config = self._get_provider_config(provider)
self.client = None
if provider == "openai" and OPENAI_OK and api_key:
openai.api_key = api_key
self.client = openai
elif provider == "google" and GOOGLE_OK and api_key:
genai.configure(api_key=api_key)
self.client = genai
elif provider == "anthropic" and ANTHROPIC_OK and api_key:
self.client = anthropic.Anthropic(api_key=api_key)
def _get_provider_config(self, provider: str) -> dict:
configs = {
"smart": {"name": "Smart AI", "status": "✅", "desc": "Instant responses - no API key needed"},
"openai": {"name": "OpenAI GPT-4o", "status": "🟢" if OPENAI_OK else "❌", "desc": "Requires API key"},
"google": {"name": "Google Gemini", "status": "🔵" if GOOGLE_OK else "❌", "desc": "Requires API key"},
"anthropic": {"name": "Anthropic Claude", "status": "🟣" if ANTHROPIC_OK else "❌", "desc": "Requires API key"},
}
return configs.get(provider, configs["smart"])
def generate(self, prompt: str, history: list = None) -> str:
"""Generate response based on provider."""
if self.provider == "smart" or not self.client:
return _smart_respond(prompt, history or [])
try:
if self.provider == "openai":
messages = [{"role": "user", "content": prompt}]
if history:
for h in history:
messages.append(h)
response = self.client.chat.completions.create(
model="gpt-4o",
messages=messages,
)
return response.choices[0].message.content
elif self.provider == "google":
model = self.client.GenerativeModel("gemini-pro")
chat = model.start_chat(history=[])
response = chat.send_message(prompt)
return response.text
elif self.provider == "anthropic":
response = self.client.messages.create(
model="claude-3-opus-20240229",
max_tokens=1024,
messages=[{"role": "user", "content": prompt}]
)
return response.content[0].text
except Exception as e:
return f"Error with {self.provider}: {str(e)}. Falling back to smart AI.\n\n" + _smart_respond(prompt, history or [])
return _smart_respond(prompt, history or [])
|