recommendation.py

from transformers import GPT2LMHeadModel, GPT2Tokenizer
import torch
def generate_movie_recommendations(input_movie_name):
    # Load pretrained GPT-2 model and tokenizer
    model_name = "gpt2"  
    model = GPT2LMHeadModel.from_pretrained(model_name)
    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
    # Move the model to GPU if available
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)

    # Generate recommendations using the language model
    input_ids = tokenizer.encode(f"Based on movie '{input_movie_name}', recommended movies:", return_tensors="pt").to(device)
    output = model.generate(input_ids, max_length=100, num_beams=5, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7)

    # Decode and print the generated recommendations
    recommendations = tokenizer.decode(output[0], skip_special_tokens=True).strip()
    return recommendations