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Browse files- .gitattributes +1 -0
- app.py +41 -0
- movies_dataset.csv +3 -0
- requirements.txt +4 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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movies_dataset.csv filter=lfs diff=lfs merge=lfs -text
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app.py
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import torch
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import gradio as gr
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import pandas as pd
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from sentence_transformers import SentenceTransformer, util
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df = pd.read_csv("movies_dataset.csv")
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df['full_text'] = (
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df['title'].fillna('') + " | " +
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df['genres'].fillna('') + " | " +
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df['overview'].fillna('') + " | " +
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df['tagline'].fillna('')
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)
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df = df.sort_values(by="popularity", ascending=False).head(5000).reset_index(drop=True)
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model = SentenceTransformer('all-MiniLM-L6-v2')
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df['full_text'] = df['full_text'].fillna('').astype(str)
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embeddings = model.encode(df['full_text'].tolist(), show_progress_bar=False)
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df['embeddings'] = embeddings.tolist()
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embedding_tensor = torch.tensor(embeddings)
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def recommend_movies(user_input, top_k=5):
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if not user_input.strip():
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return "❗Please enter a movie description or genre."
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user_embedding = model.encode(user_input, convert_to_tensor=True)
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similarities = util.cos_sim(user_embedding, embedding_tensor)[0]
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top_indices = similarities.argsort(descending=True)[:top_k]
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results = []
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for idx in top_indices:
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row = df.iloc[idx.item()]
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results.append(f"🎬 **{row['title']}**\n{row['overview'][:300]}...")
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return "\n\n".join(results)
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demo = gr.Interface(
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fn=recommend_movies,
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inputs=gr.Textbox(lines=2, placeholder="Describe a movie, mood, or genre..."),
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outputs=gr.Markdown(),
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title="🎬 Movie Recommender",
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description="Type in a description, vibe, or genre and get movie suggestions!"
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)
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demo.launch()
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movies_dataset.csv
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version https://git-lfs.github.com/spec/v1
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oid sha256:bab7686097b796fd9a5272e6712bf47c8ac6c99d58818a29e87e02bff551a5ce
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size 49724492
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requirements.txt
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@@ -0,0 +1,4 @@
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gradio
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torch
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pandas
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sentence-transformers
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