feat: add summary endpoint
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@@ -10,7 +10,9 @@ app = Flask(__name__)
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CORS(app, resources={r"/*": {"origins": "http://localhost:5173"}})
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CORS(app, resources={r"/*": {"origins": "http://localhost:5173"}})
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# Global State
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# Global State
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stat_obj = None
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posts_df = pd.read_json('posts.jsonl', lines=True)
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comments_df = pd.read_json('comments.jsonl', lines=True)
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stat_obj = StatGen(posts_df, comments_df)
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@app.route('/upload', methods=['POST'])
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@app.route('/upload', methods=['POST'])
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def upload_data():
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def upload_data():
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@@ -89,6 +91,18 @@ def search_dataset():
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query = data["query"]
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query = data["query"]
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return jsonify(stat_obj.filter_events(query).to_dict(orient='records')), 200
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return jsonify(stat_obj.filter_events(query).to_dict(orient='records')), 200
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@app.route('/stats/summary', methods=["GET"])
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def get_summary():
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if stat_obj is None:
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return jsonify({"error": "No data uploaded"}), 400
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try:
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return jsonify(stat_obj.get_summary()), 200
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except ValueError as e:
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return jsonify({"error": f"Malformed or missing data: {str(e)}"}), 400
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except Exception as e:
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return jsonify({"error": f"An unexpected error occurred: {str(e)}"}), 500
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@app.route('/reset', methods=["GET"])
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@app.route('/reset', methods=["GET"])
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def reset_dataset():
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def reset_dataset():
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if stat_obj is None:
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if stat_obj is None:
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@@ -88,7 +88,7 @@ class StatGen:
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self.df
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self.df
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.groupby('date')
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.groupby('date')
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.size()
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.size()
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.reset_index(name='posts_count')
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.reset_index(name='event_count')
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)
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)
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def filter_events(self, search_query: str) -> pd.DataFrame:
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def filter_events(self, search_query: str) -> pd.DataFrame:
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@@ -97,3 +97,17 @@ class StatGen:
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def reset_dataset(self) -> None:
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def reset_dataset(self) -> None:
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self.df = self.original_df.copy(deep=True)
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self.df = self.original_df.copy(deep=True)
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def get_summary(self) -> dict:
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return {
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"total_events": int(len(self.df)),
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"total_posts": int((self.df["type"] == "post").sum()),
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"total_comments": int((self.df["type"] == "comment").sum()),
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"unique_users": int(self.df["author"].nunique()),
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"time_range": {
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"start": int(self.df["dt"].min().timestamp()),
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"end": int(self.df["dt"].max().timestamp())
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}
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}
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