Update inference.py

inference.py

@@ -129,3 +129,33 @@ def get_system_stats():
         "gpu_memory_used_gb": round(torch.cuda.memory_allocated() / (1024 ** 3), 2) if gpu_info else "N/A",
         "platform": platform.platform()
     }
+
+# 🧪 Fine-tune Evo from feedback data (CSV or in-memory list)
+def retrain_from_feedback(feedback_data):
+    if not feedback_data:
+        return "⚠️ No feedback data to retrain from."
+
+    model = load_model()
+    if model is None:
+        return "❌ Evo model not available."
+
+    model.train()
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+
+    for row in feedback_data:
+        question, opt1, opt2, answer, *_ = row
+        label = torch.tensor([1.0 if answer.strip() == opt2.strip() else 0.0])  # opt2 is class 1
+
+        # Build input pair
+        input_text = f"{question} [SEP] {opt2 if label.item() == 1 else opt1}"
+        encoded = tokenizer(input_text, return_tensors="pt", padding="max_length", truncation=True, max_length=128)
+
+        logits = model(encoded["input_ids"])
+        loss = F.binary_cross_entropy_with_logits(logits.squeeze(), label)
+        loss.backward()
+        optimizer.step()
+        optimizer.zero_grad()
+
+    torch.save(model.state_dict(), MODEL_PATH)
+    return "✅ Evo retrained from feedback."
+