Update inference.py

inference.py

@@ -1,6 +1,13 @@
 # inference.py
 
+import os
+import csv
+import random
 import torch
+import pandas as pd
+import psutil
+import platform
+import GPUtil
 import torch.nn.functional as F
 from transformers import AutoTokenizer
 from evo_model import EvoTransformerV22
@@ -11,23 +18,16 @@ from evo_architecture import (
     save_best_genome,
     load_best_genome
 )
-import random
-import csv
-import os
-import psutil
-import platform
-import GPUtil
 import openai
-import pandas as pd
 
-# 🔐 Load OpenAI key
+# 🔐 API Key
 openai.api_key = os.getenv("OPENAI_API_KEY", "sk-...")
 
-# ⚙️ Runtime setup
+# 📦 Setup
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-# 🔁 Mutable model & config
+# 🔁 Current genome state
 current_config = load_best_genome()
 model = build_model_from_config(current_config).to(device)
 model.eval()
@@ -50,7 +50,7 @@ def evo_chat_predict(history, question, options):
         "context_used": question
     }
 
-# 🤖 GPT comparison
+# 🤖 GPT response
 def get_gpt_response(prompt):
     try:
         client = openai.OpenAI()
@@ -62,7 +62,7 @@ def get_gpt_response(prompt):
     except Exception as e:
         return f"(GPT Error) {e}"
 
-# 📊 Evo stats
+# 📊 Genome stats
 def get_model_config():
     return {
         "num_layers": current_config.get("num_layers", "?"),
@@ -96,7 +96,7 @@ def get_system_stats():
         "platform": platform.platform()
     }
 
-# 🔁 Retrain from feedback
+# 🔁 Evo retrain logic
 def retrain_from_feedback_csv():
     global current_config, model
 
@@ -104,16 +104,13 @@ def retrain_from_feedback_csv():
         return "⚠️ No feedback log found."
 
     df = pd.read_csv(FEEDBACK_LOG)
-
-    # Validate votes
-    if df.empty or "vote" not in df.columns or df["vote"].dropna().empty:
+    if df.empty or "vote" not in df.columns:
         return "⚠️ No usable feedback data. Please vote on Evo or GPT."
 
     df = df[df["vote"].isin(["Evo", "GPT"])]
     if df.empty:
         return "⚠️ No usable feedback data. Please vote on Evo or GPT."
 
-    # Prepare training data
     data = []
     for _, row in df.iterrows():
         label = 1 if row["vote"] == "Evo" else 0
@@ -123,13 +120,12 @@ def retrain_from_feedback_csv():
     if not data:
         return "⚠️ No usable feedback data."
 
-    # Mutate config
     new_config = mutate_genome(current_config)
     model = build_model_from_config(new_config).to(device)
     current_config = new_config
     log_genome(new_config)
 
-    # Fine-tune model
+    # 🔁 Fine-tune
     model.train()
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
     for epoch in range(3):
@@ -148,7 +144,7 @@ def retrain_from_feedback_csv():
     save_best_genome({**new_config, "accuracy": "Live-Finetuned"})
     return f"✅ Evo retrained on {len(data)} feedback entries."
 
-# 🔄 Reload model
+# 🔄 Reload trigger
 def load_model(force_reload=False):
     global model
-    model.eval()
+    model.eval()