src/recommendation_utils.py

import streamlit as st
import numpy as np
import pandas as pd
import pickle
#from keras.models import model_from_json
#from keras.optimizers import Adam

# ─────────────────────────────────────────────────────────────────────────────
# Neural Network Model Laden (aus JSON + Weights)
# ─────────────────────────────────────────────────────────────────────────────
def load_nn_model(config_path, weights_path):
    with open(config_path, "r") as f:
        model_json = f.read()
    model = model_from_json(model_json)
    model.load_weights(weights_path)
    model.compile(optimizer=Adam(), loss="mse", metrics=["mse", "mae"])
    return model

# ─────────────────────────────────────────────────────────────────────────────
# SVD Model & Trainset Laden
# ─────────────────────────────────────────────────────────────────────────────
@st.cache_resource
def load_svd_model(path="models/svd_model.pkl"):
    with open(path, "rb") as f:
        return pickle.load(f)

@st.cache_resource
def load_trainset(path="models/trainset.pkl"):
    with open(path, "rb") as f:
        return pickle.load(f)


# ─────────────────────────────────────────────────────────────────────────────
# Encodings (dict mit user/movie Encodings) laden
# ─────────────────────────────────────────────────────────────────────────────
def load_encodings(path="encodings.pkl"):
    with open(path, "rb") as f:
        return pickle.load(f)

# ─────────────────────────────────────────────────────────────────────────────
# SVD Recommendation
# ─────────────────────────────────────────────────────────────────────────────
def fold_in_new_user(model, trainset, user_ratings, reg=5):
    n_factors = model.n_factors
    A = np.zeros((n_factors, n_factors))
    b = np.zeros(n_factors)
    bias_numerator, bias_denominator = 0, 0
    global_mean = trainset.global_mean

    for movie_id, rating in user_ratings.items():
        try:
            movie_id = int(movie_id)
            inner_iid = trainset.to_inner_iid(movie_id)
            qi = model.qi[inner_iid]
            bi = model.bi[inner_iid]
            A += np.outer(qi, qi)
            b += qi * (rating - global_mean - bi)
            bias_numerator += (rating - global_mean - bi)
            bias_denominator += 1
        except ValueError:
            continue

    A += reg * np.eye(n_factors)
    new_user_factors = np.linalg.solve(A, b) if np.linalg.det(A) != 0 else np.linalg.pinv(A) @ b
    new_user_bias = bias_numerator / bias_denominator if bias_denominator > 0 else 0
    return new_user_factors, new_user_bias

def recommend_with_svd(model, trainset, ratings_df, user_ratings, top_n=10):
    new_user_factors, new_user_bias = fold_in_new_user(model, trainset, user_ratings)
    global_mean = trainset.global_mean
    movie_predictions = []
    for inner_iid in range(trainset.n_items):
        movie_id = trainset.to_raw_iid(inner_iid)
        if movie_id not in user_ratings:
            pred = global_mean + new_user_bias + model.bi[inner_iid] + np.dot(new_user_factors, model.qi[inner_iid])
            movie_predictions.append({'movieId': int(movie_id), 'rating': pred})
    df = pd.DataFrame(movie_predictions)
    df["rating"] = df["rating"].round(4)  # optional: Ratings runden für mehr Stabilität
    df = df.sort_values(["rating", "movieId"], ascending=[False, True]).head(top_n)

    return df

# ─────────────────────────────────────────────────────────────────────────────
# Neural Network Recommendation
# ─────────────────────────────────────────────────────────────────────────────
def recommend_with_nn(user_ratings, model, available_movies, top_n=10):
    """
    Args:
        user_ratings: dict of movieId → rating
        model: compiled Keras model
        available_movies: list of movieIds
        top_n: number of recommendations
    Returns:
        DataFrame with movieId and predicted rating
    """
    if not available_movies:
        return pd.DataFrame(columns=["movieId", "rating"])

    user_id = max(user_ratings.keys(), default=0) + 100000  # Dummy new user
    user_vector = np.array([user_id] * len(available_movies))
    movie_vector = np.array(available_movies)

    predictions = model.predict([user_vector, movie_vector], verbose=0)
    df = pd.DataFrame({
        'movieId': available_movies,
        'rating': predictions.flatten()
    })
    df = df[~df["movieId"].isin(user_ratings.keys())]
    return df.sort_values("rating", ascending=False).head(top_n)