Update app.py

app.py

@@ -1,3 +1,4 @@
+from openboxing_api import find_champion_by_name, get_bouts_for_champion
 import gradio as gr
 from huggingface_hub import InferenceClient
 
@@ -46,6 +47,28 @@ def respond(
     temperature,
     top_p,
     hf_token: gr.OAuthToken,
+    if "predict" in message.lower() and "vs" in message.lower():
+    fighters = message.split("vs")
+    if len(fighters) == 2:
+        fighter1_name = fighters[0].strip()
+        fighter2_name = fighters[1].strip()
+
+        fighter1 = find_champion_by_name(fighter1_name)
+        fighter2 = find_champion_by_name(fighter2_name)
+
+        if fighter1 and fighter2:
+            history1 = get_bouts_for_champion(fighter1["championId"])
+            history2 = get_bouts_for_champion(fighter2["championId"])
+
+            # Build structured prompt for LLM
+            message = f"""
+            Fighter 1: {fighter1['name']['first']} {fighter1['name']['last']}
+            Fighter 2: {fighter2['name']['first']} {fighter2['name']['last']}
+            Fighter 1 bouts: {len(history1)}
+            Fighter 2 bouts: {len(history2)}
+            Predict this fight round by round.
+            """
+
 ):
 
     # Initialize model client (DeepSeek 70B)
@@ -123,3 +146,20 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     demo.launch()
+from openboxing_api import find_champion_by_name, get_bouts_for_champion
+
+fighter1 = find_champion_by_name("Isaac Cruz")
+fighter2 = find_champion_by_name("Lemont Roach")
+
+if fighter1 and fighter2:
+    history1 = get_bouts_for_champion(fighter1["championId"])
+    history2 = get_bouts_for_champion(fighter2["championId"])
+    
+    # Build your prompt for LLM
+    prompt = f"""
+    Fighter 1: {fighter1['name']['first']} {fighter1['name']['last']}
+    Fighter 2: {fighter2['name']['first']} {fighter2['name']['last']}
+    Fighter 1 bouts: {len(history1)}
+    Fighter 2 bouts: {len(history2)}
+    Predict this fight round by round.
+    """