Create main.py

main.py

@@ -0,0 +1,58 @@
+from fastapi import FastAPI
+from pydantic import BaseModel
+import joblib
+import pandas as pd
+
+# Load model
+model = joblib.load("speed_hit_model.pkl")
+
+# Label decoding
+label_reverse = {0: "Player attacks twice and counters twice", 
+                 1: "Enemy attacks twice and counters twice", 
+                 2: "Both attack once"}
+
+# Define API input structure
+class BattleStats(BaseModel):
+    player_speed: int
+    player_weight: int
+    player_attack_accuracy: float
+    player_hit_accuracy: float
+    player_avoidance: float
+    enemy_speed: int
+    enemy_weight: int
+    enemy_attack_accuracy: float
+    enemy_hit_accuracy: float
+    enemy_avoidance: float
+
+# Initialize FastAPI app
+app = FastAPI()
+
+@app.post("/predict")
+def predict(stats: BattleStats):
+    # Compute derived features
+    player_base_speed = stats.player_speed - stats.player_weight
+    enemy_base_speed = stats.enemy_speed - stats.enemy_weight
+    player_hit_chance = stats.player_attack_accuracy * stats.player_hit_accuracy * (1 - stats.enemy_avoidance)
+    enemy_hit_chance = stats.enemy_attack_accuracy * stats.enemy_hit_accuracy * (1 - stats.player_avoidance)
+
+    # Create DataFrame
+    features = pd.DataFrame([{
+        "Player Speed": stats.player_speed,
+        "Player Weight": stats.player_weight,
+        "Player Base Speed": player_base_speed,
+        "Player Attack Accuracy": stats.player_attack_accuracy,
+        "Player Hit Accuracy": stats.player_hit_accuracy,
+        "Player Avoidance": stats.player_avoidance,
+        "Player Hit Chance": player_hit_chance,
+
+        "Enemy Speed": stats.enemy_speed,
+        "Enemy Weight": stats.enemy_weight,
+        "Enemy Base Speed": enemy_base_speed,
+        "Enemy Attack Accuracy": stats.enemy_attack_accuracy,
+        "Enemy Hit Accuracy": stats.enemy_hit_accuracy,
+        "Enemy Avoidance": stats.enemy_avoidance,
+        "Enemy Hit Chance": enemy_hit_chance
+    }])
+
+    pred = model.predict(features)[0]
+    return {"outcome": label_reverse[pred]}