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Update app.py
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app.py
CHANGED
@@ -63,7 +63,10 @@ def get_similar_and_recommend(input_text):
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most_similar_index = np.argmax(similarities)
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# Get all features of the most similar video
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-
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# Recommend the top 10 videos based on GNN embeddings and dot product
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def recommend_next_10_videos(given_video_index, all_video_embeddings):
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@@ -73,14 +76,20 @@ def get_similar_and_recommend(input_text):
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]
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dot_products[given_video_index] = -float("inf")
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top_10_indices = np.argsort(dot_products)[::-1][:10]
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return [df.iloc[idx].to_dict() for idx in top_10_indices]
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top_10_recommended_videos_features = recommend_next_10_videos(
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most_similar_index, all_video_embeddings
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)
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#
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output = {
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"most_similar_video": most_similar_video_features,
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"top_10_recommended_videos": top_10_recommended_videos_features,
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@@ -88,13 +97,13 @@ def get_similar_and_recommend(input_text):
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return output
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# Update the Gradio interface to output a JSON object
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interface = gr.Interface(
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fn=get_similar_and_recommend,
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inputs=gr.components.Textbox(label="Enter Text to Find Most Similar Video"),
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outputs=gr.JSON(),
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title="Video Recommendation System with GNN-based Recommendations",
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description="Enter text to find the most similar video and get the top 10 recommended videos with all features in a JSON object.",
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)
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interface.launch()
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most_similar_index = np.argmax(similarities)
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# Get all features of the most similar video
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unwanted_keys = ["text_for_embedding", "embeddings"]
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for key in unwanted_keys:
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if key in most_similar_video_features:
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del most_similar_video_features[key]
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# Recommend the top 10 videos based on GNN embeddings and dot product
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def recommend_next_10_videos(given_video_index, all_video_embeddings):
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]
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dot_products[given_video_index] = -float("inf")
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top_10_indices = np.argsort(dot_products)[[::-1][:10]
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return [df.iloc[idx].to_dict() for idx in top_10_indices]
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top_10_recommended_videos_features = recommend_next_10_videos(
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most_similar_index, all_video_embeddings
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)
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# Exclude unwanted features for recommended videos
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for recommended_video in top_10_recommended_videos_features:
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for key in unwanted_keys:
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if key in recommended_video:
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del recommended_video[key]
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# Create the output JSON with all features except the unwanted ones
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output = {
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"most_similar_video": most_similar_video_features,
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"top_10_recommended_videos": top_10_recommended_videos_features,
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return output
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# Update the Gradio interface to output a JSON object without unwanted features
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interface = gr.Interface(
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fn=get_similar_and_recommend,
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inputs=gr.components.Textbox(label="Enter Text to Find Most Similar Video"),
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outputs=gr.JSON(),
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title="Video Recommendation System with GNN-based Recommendations",
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description="Enter text to find the most similar video and get the top 10 recommended videos with all features except embeddings-related fields in a JSON object.",
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)
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interface.launch()
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