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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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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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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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recommendations = recommendations[:5]
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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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