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app.py

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+import gradio as gr
+import onnxruntime as ort
+import numpy as np
+import pickle
+import re
+
+# Load the ONNX model
+onnx_model_path = "formation_predictor.onnx"
+ort_session = ort.InferenceSession(onnx_model_path)
+
+# Function to convert input data to one-hot encoding
+def to_one_hot(indices, num_classes):
+    indices = np.array(indices, dtype=int)
+    return np.eye(num_classes)[indices]
+
+# Load the label encoder
+def load_label_encoder():
+    with open("label_encoder.pkl", "rb") as f:
+        le = pickle.load(f)
+    return le
+
+le = load_label_encoder()
+num_classes = len(le.classes_)
+
+# Function to prepare input data
+def prepare_input(opponent_formation, le, num_classes):
+    opponent_formation = opponent_formation.strip().strip("'\"[]")  # Ensure no leading/trailing spaces, quotes, or brackets
+    opp_idx = le.transform([opponent_formation])[0] if isinstance(opponent_formation, str) else opponent_formation
+    opp_one_hot = to_one_hot([opp_idx], num_classes)
+    return opp_one_hot
+
+# Function to recommend formation using ONNX model
+def recommend_formation_onnx(opponent_formation, ort_session, le, num_classes):
+    opp_one_hot = prepare_input(opponent_formation, le, num_classes)
+    
+    best_formation, best_score = None, -float("inf")
+    evaluated_formations = []
+    for our_idx in range(num_classes):
+        our_one_hot = to_one_hot([our_idx], num_classes)
+        input_vector = np.concatenate([opp_one_hot, our_one_hot], axis=1).astype(np.float32)
+        
+        # Run the ONNX model
+        ort_inputs = {ort_session.get_inputs()[0].name: input_vector}
+        ort_outs = ort_session.run(None, ort_inputs)
+        score = ort_outs[0][0, 0]
+        
+        formation = le.inverse_transform([our_idx])[0]
+        evaluated_formations.append((formation, score))
+        
+        if score > best_score:
+            best_score = score
+            best_formation = formation
+    
+    evaluated_formations.sort(key=lambda x: x[1], reverse=True)
+    return best_formation, evaluated_formations
+
+# Function to handle the recommend button click
+def recommend(opponent_formation):
+    opponent_formation = opponent_formation.strip().strip("'\"[]")  # Ensure no leading/trailing spaces, quotes, or brackets
+    
+    # Validate the format of the opponent formation
+    if not re.match(r'^\d+(-\d+)+$', opponent_formation):
+        return f"Error: Formation '{opponent_formation}' is not in the correct format (e.g., '3-4-2-1').", []
+    
+    if opponent_formation not in le.classes_:
+        return f"Error: Formation '{opponent_formation}' not recognized.", []
+    
+    best_formation, evaluated_formations = recommend_formation_onnx(opponent_formation, ort_session, le, num_classes)
+    return f"Recommended formation: {best_formation}", evaluated_formations
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=recommend,
+    inputs=gr.inputs.Textbox(lines=1, placeholder="Enter opponent formation (e.g., '3-4-2-1')"),
+    outputs=[
+        gr.outputs.Textbox(label="Recommended Formation"),
+        gr.outputs.Dataframe(headers=["Formation", "Score"], label="Evaluated Formations")
+    ],
+    title="Deepfield Proyecto Maradona E3 Football Formation Recommender",
+    description="Enter the opponent formation to get the recommended formation and a list of evaluated formations with their scores."
+)
+
+# Launch the Gradio interface
+iface.launch()

formation_predictor.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1de5260c6c8457b12da0c2930bc5565b55099bdad0831cab031a690bd8244d94
+size 18124

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c26c641ba961031566abddac21718a0bd1e01e1e1e31022a08a81b594c7095fd
+size 402

requirements.txt

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+gradio
+onnxruntime
+numpy
+scikit-learn