Update inference.py

inference.py

@@ -10,8 +10,6 @@ import psutil
 import platform
 import GPUtil
 import openai
-import GPUtil
-
 
 # Load tokenizer
 tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
@@ -45,12 +43,14 @@ def evo_chat_predict(history, question, options):
 
 def get_gpt_response(prompt):
     openai.api_key = os.getenv("OPENAI_API_KEY", "sk-...")
+
     try:
-        res = openai.ChatCompletion.create(
+        client = openai.OpenAI()
+        response = client.chat.completions.create(
             model="gpt-3.5-turbo",
             messages=[{"role": "user", "content": prompt}]
         )
-        return res.choices[0].message["content"]
+        return response.choices[0].message.content.strip()
     except Exception as e:
         return f"(GPT Error) {e}"
 
@@ -94,8 +94,9 @@ def retrain_from_feedback_csv():
     with open(FEEDBACK_LOG, "r", encoding="utf-8") as f:
         reader = csv.DictReader(f)
         for row in reader:
-            if row.get("vote") in ["Evo", "GPT"]:
-                label = 1 if row["vote"] == "Evo" else 0
+            vote = row.get("user_preference") or row.get("vote")
+            if vote in ["Evo", "GPT"]:
+                label = 1 if vote == "Evo" else 0
                 input_text = f"{row['question']} {row['option1']} {row['option2']}"
                 data.append((input_text, label))
 
@@ -114,17 +115,17 @@ def retrain_from_feedback_csv():
     optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
     for epoch in range(3):
         random.shuffle(data)
-        total_loss = 0.0
         for text, label in data:
             enc = tokenizer(text, padding="max_length", truncation=True, max_length=128, return_tensors="pt").to(device)
             input_ids = enc["input_ids"]
             label_tensor = torch.tensor([label], dtype=torch.float32).to(device)
             logits = model(input_ids)
+            if logits.ndim == 2:
+                logits = logits.squeeze(1)
             loss = F.binary_cross_entropy_with_logits(logits.squeeze(), label_tensor)
             optimizer.zero_grad()
             loss.backward()
             optimizer.step()
-            total_loss += loss.item()
     model.eval()
     return f"✅ Evo retrained on {len(data)} feedback entries."