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Browse files- .gitattributes +35 -35
- README.md +13 -13
- ScriptMatcher.py +98 -0
- __init__.py +0 -0
- app.py +31 -0
- models/Similarity_K_Dataset/K_Dataset.csv +0 -0
- models/Similarity_K_Dataset/plot_embeddings.npy +3 -0
- models/Similarity_K_Dataset/synopsis_embeddings.npy +3 -0
- requirements.txt +6 -0
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README.md
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---
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title: Script Similarity
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emoji: 🦀
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colorFrom: yellow
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colorTo: gray
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sdk: gradio
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sdk_version: 4.29.0
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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---
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title: Script Similarity
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emoji: 🦀
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colorFrom: yellow
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colorTo: gray
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sdk: gradio
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sdk_version: 4.29.0
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app_file: app.py
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pinned: false
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license: mit
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---
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Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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ScriptMatcher.py
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import pandas as pd
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import numpy as np
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from ast import literal_eval
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import yake
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import spacy
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from sklearn.metrics.pairwise import cosine_similarity
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from sentence_transformers import SentenceTransformer
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import os
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class ScriptMatcher:
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def __init__(self, data_path = None, model_name='paraphrase-mpnet-base-v2',dataframe = None):
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"""
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Initialize the SeriesMatcher object.
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Parameters:
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data_path (str): Path to the dataset file.
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model_name (str): Name of the sentence transformer model. Default is 'paraphrase-mpnet-base-v2'.
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"""
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if data_path is not None:
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self.dataset = pd.read_csv(data_path)
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if dataframe is not None:
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self.dataset = dataframe
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self.model = SentenceTransformer(model_name)
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self.kw_extractor = yake.KeywordExtractor("en", n=1, dedupLim=0.9)
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self.k_dataset = pd.read_csv('models/Similarity_K_Dataset/K_Dataset.csv')
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self._ent_type = ["PERSON","NORP","FAC","ORG","GPE","LOC","PRODUCT","EVENT","WORK","ART","LAW",
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"LANGUAGE","DATE","TIME","PERCENT","MONEY","QUANTITY","ORDINAL","CARDINAL"]
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self.embeddings_synopsis_list = np.load("models/Similarity_K_Dataset/plot_embeddings.npy")
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self.plot_embedding_list = np.load("models/Similarity_K_Dataset/synopsis_embeddings.npy")
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try:
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self.nlp = spacy.load("en_core_web_sm")
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except:
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print("Downloading spaCy NLP model...")
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os.system(
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"pip install https://huggingface.co/spacy/en_core_web_sm/resolve/main/en_core_web_sm-any-py3-none-any.whl")
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self.nlp = spacy.load("en_core_web_sm")
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def extract_keywords(self, text):
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"""
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Extract keywords from a given text using the YAKE keyword extraction algorithm.
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Parameters:
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text (str): Text from which to extract keywords.
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Returns:
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str: A string of extracted keywords joined by spaces.
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"""
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extracted_keywords = self.kw_extractor.extract_keywords(text)
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return " ".join([keywords[0] for keywords in extracted_keywords if keywords[0] not in self._ent_type])
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def preprocess_text(self, text):
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"""
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Process a given text to replace named entities and extract keywords.
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Parameters:
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text (str): The text to process.
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Returns:
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str: Processed text with named entities replaced and keywords extracted.
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"""
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doc = self.nlp(text)
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replaced_text = text
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for token in doc:
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if token.ent_type_ != "MISC" and token.ent_type_ != "":
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replaced_text = replaced_text.replace(token.text, f"<{token.ent_type_}>")
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return self.extract_keywords(replaced_text)
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def find_similar_series(self, new_synopsis, genres_keywords,k=5):
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"""
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Find series similar to a new synopsis.
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Parameters:
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new_synopsis (str): The synopsis to compare.
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k (int): The number of similar series to return.
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Returns:
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pd.DataFrame: A dataframe of the closest series.
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"""
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processed_synopsis = self.preprocess_text(new_synopsis)
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genre_keywords = " ".join(genres_keywords)
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print(genre_keywords)
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synopsis_sentence = genre_keywords + self.extract_keywords(processed_synopsis)
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synopsis_embedding = self.model.encode([synopsis_sentence])
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cosine_similarity_matrix = 0.75 * cosine_similarity(synopsis_embedding, self.embeddings_synopsis_list) + 0.25 * cosine_similarity(synopsis_embedding,self.plot_embedding_list)
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top_k_indices = cosine_similarity_matrix.argsort()[0, -k:][::-1]
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closest_series = self.k_dataset.iloc[top_k_indices]
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# Add scores column
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closest_series["Score"] = cosine_similarity_matrix[0, top_k_indices]
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return closest_series[["Series", "Genre","Score"]].to_dict(orient='records')
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__init__.py
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app.py
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import gradio as gr
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from ScriptMatcher import ScriptMatcher
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# Initialize the ScriptMatcher instance
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scriptmatcher = ScriptMatcher()
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def classify_movie_genre(description, genres):
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"""
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Given a description (synopsis) and genres, return similar series predictions.
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"""
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# Split the genres string into a list of keywords
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genre_keywords = genres.split(",") # Assuming genres are comma-separated
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# Get the predictions using the ScriptMatcher
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predictions = scriptmatcher.find_similar_series(description, genre_keywords)
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return predictions
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# Create the Gradio interface
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iface = gr.Interface(
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fn=classify_movie_genre,
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inputs=[
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gr.Textbox(lines=5, label="Synopsis (Description)"),
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gr.Textbox(label="Genres (Comma-separated)")
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],
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outputs=gr.Dataframe(label="Similar Series Predictions"),
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live=False, # No need for live updates as the processing will be based on submission
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title="Genre Prediction",
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description="Provide a movie synopsis and genres to get predictions for similar scripts.",
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)
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# Launch the Gradio interface
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iface.launch(inline=False)
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models/Similarity_K_Dataset/K_Dataset.csv
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models/Similarity_K_Dataset/plot_embeddings.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:dc05423932e01a2907ce69a9832010d116ee64d86e2a19a97bdf28846fd39c92
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size 5222528
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models/Similarity_K_Dataset/synopsis_embeddings.npy
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version https://git-lfs.github.com/spec/v1
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oid sha256:b48d966a4993e82122d09441875c93a60f47aca960cee908220d1daf5eba7c92
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size 5222528
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requirements.txt
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pandas==2.2.1
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numpy==1.26.4
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yake==0.4.8
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spacy==3.7.4
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scikit-learn==1.2.2
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sentence-transformers==2.6.1
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