| """ |
| 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 []) |
|
|
