Update app.py

app.py

@@ -11,6 +11,7 @@ import torch.nn.functional as F
 from transformers import AutoModelForCausalLM, AutoProcessor, AutoTokenizer, TextIteratorStreamer, AutoModel, AutoModelForSequenceClassification
 from kernels import get_kernel
 from typing import Any, Optional, Dict
+import numpy as np
 
 
 # Login to HF to get access to the model weights
@@ -78,11 +79,20 @@ def bot(user_message: str, history: list[dict[str, Any]]):
 
     res = ""
     history = history + [{"role": "user", "content": user_message}]
+    scores = {}
     for model_choice in MODEL_OPTIONS:
         model, tokenizer = load_model(model_choice)  # returns embedding model
         score = compute_score(user_message, model, tokenizer)["score"]
+        scores["model_choice"] = score
         res += f"{model_choice}: {score}\n"
 
+
+
+    formula_score = np.median([scores["lapa-llm/fineweb-nemotron-edu-score"], scores["lapa-llm/fineweb-mixtral-edu-score"], scores["lapa-llm/fasttext-quality-score"],]) \
+    * item["lapa-llm/alignment-score-model"] * scores["lapa-llm/manipulative-score-model"] * scores["lapa-llm/gec-score-model"]
+
+    res += f"Formula (combined) score: {formula_score}\n"
+
     history.append({"role": "assistant", "content": res.strip()})
     return "", history