Artificial Intelligence Services

# Custom Model Training
import tensorflow as tf
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Dense(128, activation='relu', input_shape=(input_dim,)),
    layers.Dropout(0.3),
    layers.Dense(64, activation='relu'),
    layers.Dense(num_classes, activation='softmax')
])

model.compile(
    optimizer='adam',
    loss='categorical_crossentropy',
    metrics=['accuracy']
)

history = model.fit(X_train, y_train, 
                   validation_data=(X_val, y_val),
                   epochs=100, batch_size=32)

# NLP Pipeline
from transformers import pipeline, AutoTokenizer, AutoModel

# Sentiment Analysis
sentiment_analyzer = pipeline("sentiment-analysis")
result = sentiment_analyzer("This product is amazing!")

# Named Entity Recognition
ner = pipeline("ner", aggregation_strategy="simple")
entities = ner("Apple Inc. was founded by Steve Jobs in California.")

# Custom Text Classification
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")

def classify_text(text):
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    return predictions

# Recommendation System
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

class RecommendationEngine:
    def __init__(self):
        self.vectorizer = TfidfVectorizer(stop_words='english')
        self.similarity_matrix = None
        
    def fit(self, items_df):
        # Content-based filtering
        tfidf_matrix = self.vectorizer.fit_transform(items_df['description'])
        self.similarity_matrix = cosine_similarity(tfidf_matrix)
        
    def recommend(self, item_id, num_recommendations=5):
        item_index = self.items_df.index[self.items_df['id'] == item_id][0]
        similarity_scores = list(enumerate(self.similarity_matrix[item_index]))
        similarity_scores = sorted(similarity_scores, key=lambda x: x[1], reverse=True)
        
        recommended_indices = [i[0] for i in similarity_scores[1:num_recommendations+1]]
        return self.items_df.iloc[recommended_indices]

# RAG Implementation
import openai
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

# Initialize embeddings and vector store
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_documents(documents, embeddings, index_name="knowledge-base")

# Create RAG chain
llm = OpenAI(temperature=0)
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever(search_kwargs={"k": 3}),
    return_source_documents=True
)

# Query with context
def query_with_context(question):
    result = qa_chain({"query": question})
    return {
        "answer": result["result"],
        "sources": result["source_documents"]
    }

# Predictive Analytics
import pandas as pd
from prophet import Prophet
import numpy as np
from sklearn.ensemble import RandomForestRegressor

# Time Series Forecasting
def forecast_sales(historical_data):
    df = historical_data[['date', 'sales']].rename(columns={'date': 'ds', 'sales': 'y'})
    
    model = Prophet(
        yearly_seasonality=True,
        weekly_seasonality=True,
        daily_seasonality=False
    )
    model.fit(df)
    
    # Create future dataframe
    future = model.make_future_dataframe(periods=30)
    forecast = model.predict(future)
    
    return forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']]

# Anomaly Detection
def detect_anomalies(data, threshold=2):
    mean = np.mean(data)
    std = np.std(data)
    anomalies = []
    
    for i, value in enumerate(data):
        z_score = abs((value - mean) / std)
        if z_score > threshold:
            anomalies.append(i)
    
    return anomalies