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