first

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.11
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the current directory contents into the container at /app
+COPY . ./app
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Define environment variable
+ENV NAME FlaskApp
+
+# Run app.py when the container launches
+CMD ["python", "app.py"]

app/app.py

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+from flask import Flask, request, jsonify
+import pickle
+import pandas as pd
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+
+# Load the graph and data
+with open("models/card_graph.pkl", "rb") as f:
+    G = pickle.load(f)
+df = pd.read_pickle("data/oracle_cards_graph.pkl")
+
+
+# Define the endpoint to get closest card matches
+@app.route("/api/get_matches", methods=["POST"])
+def get_closest_matches():
+    data = request.get_json()
+    card_name = data.get("card_name", "")
+    num_matches = data.get("num_matches", 5)
+
+    # Check if the card exists in the graph
+    if card_name not in G:
+        return jsonify({"error": f"Card '{card_name}' not found in the graph."}), 404
+
+    # Get neighbors (similar cards) and their corresponding weights
+    neighbors = G[card_name]
+    sorted_neighbors = sorted(
+        neighbors.items(), key=lambda x: x[1]["weight"], reverse=True
+    )
+    closest_matches = sorted_neighbors[:num_matches]
+
+    # Create a list of closest matches
+    matches_info = []
+    for match in closest_matches:
+        matched_card = df[df["name"] == match[0]].iloc[0]
+        match_info = {
+            "name": matched_card["name"],
+            "match_score": match[1]["weight"],
+        }
+        matches_info.append(match_info)
+
+    # Return the list of matches as JSON
+    return jsonify(matches_info)
+
+
+if __name__ == "__main__":
+    app.run(debug=True, host="0.0.0.0", port=7860)

requirements.txt

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+Flask
+pandas
+Flask-Cors
+networkx