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# !pip install --upgrade llama-index llama-index-embeddings-huggingface llama-index-llms-huggingface transformers torch accelerate bitsandbytes
from llama_index.core import Settings, SimpleDirectoryReader, VectorStoreIndex, Document
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.huggingface import HuggingFaceLLM
import torch
import os
import pandas as pd
import gradio as gr
Settings.embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
Settings.embed_model = HuggingFaceEmbedding(
model_name="sentence-transformers/all-MiniLM-L6-v2")
Settings.llm = HuggingFaceLLM(
model_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
tokenizer_name="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
context_window=2048,
max_new_tokens=256,
device_map="auto",
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}
)
csv_file_path = "movie_recommendations_with_names.csv"
df = None
try:
df = pd.read_csv(csv_file_path)
except FileNotFoundError:
print(f"Error: CSV file not found at {csv_file_path}")
print(f"Current working directory: {os.getcwd()}")
except Exception as e:
print(f"An unexpected error occurred while reading the CSV: {e}")
if df is not None:
movies_data = [
Document(text=f"MovieID: {row['movie_id']}, Title: {row['title']}, Genre: {row['genre']}, Rating: {row['rating']}",
metadata={"movie_id": row['movie_id'], "title": row['title'], "genre": row['genre'], "rating": row['rating']})
for index, row in df.iterrows()
]
index = VectorStoreIndex.from_documents(movies_data)
query_engine = index.as_query_engine()
def recommend_movie(genre):
if not genre.strip():
return "! Please enter a movie genre."
response = query_engine.query(
f"List titles and genre of movies with genre {genre}."
f"provide at least 3 recommendations if avalabile."
)
response_lines= str(response).split("\n")
filtered = [
line for line in response_lines
if line.strip()
and "Note : The query is not specific" not in line
]
recommendations = []
for rec in filtered:
if "Title:" in rec:
try:
title = rec.split("Title:")[1].split(",")[0].strip()
recommendations.append(f"{title}")
except:
recommendations.append(f"{rec.strip()}")
recommendations = recommendations[:5]
if not recommendations:
return " Sorry , I couldn't find movies for that genre."
return "\n".join(recommendations)
#gradio ui
interface = gr.Interface(
fn=recommend_movie,
inputs=gr.Textbox(
label="What type of movie are you in the mood for?",
placeholder="e.g. Action, Comedy, Drama, Sci-Fi"
),
outputs=gr.Textbox(label="🍿 Movie Recommendations"),
title="🎥 MovieRecBot",
description="Movie recommendation system powered by LlamaIndex + TinyLlama (Hugging Face)",
examples=[["Action"], ["Comedy"], ["Romance"], ["Sci-Fi"]],
)
interface.launch(share=True)
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