Update app.py

app.py

@@ -7,7 +7,6 @@ import outlines
 import pandas as pd
 import spaces
 import torch
-from outlines import generate, models, samplers
 from peft import PeftConfig, PeftModel
 from pydantic import BaseModel, ConfigDict
 from transformers import (
@@ -92,11 +91,7 @@ def get_model_and_tokenizer(model_id: str, device_map: str = "auto", quantizatio
         )
         
         # Convert to outlines model
-        outlines_model = models.transformers(
-            model,
-            tokenizer=tokenizer,
-            device_map=device_map,
-        )
+        outlines_model = outlines.models.Transformers(model, tokenizer=tokenizer)
         result = (outlines_model, tokenizer, "generation")
     
     _model_cache[model_id] = result
@@ -126,9 +121,8 @@ def label_single_response_with_model(model_id, story, question, criteria, respon
             predicted_class = torch.argmax(logits, dim=1).item()
             return str(predicted_class)
         else:
-            # For generative models
-            sampler = samplers.greedy()
-            generator = generate.json(model, ResponseModel, sampler=sampler)
+            # For generative models - using the new Outlines API
+            generator = outlines.generate.json(model, ResponseModel)
             result = generator(prompt)
             return result.score
     except Exception as e:
@@ -152,8 +146,8 @@ def label_multi_responses_with_model(model_id, story, question, criteria, respon
             predicted_classes = torch.argmax(logits, dim=1).tolist()
             scores = [str(cls) for cls in predicted_classes]
         else:
-            sampler = samplers.greedy()
-            generator = generate.json(model, ResponseModel, sampler=sampler)
+            # For generative models - using the new Outlines API
+            generator = outlines.generate.json(model, ResponseModel)
             results = generator(prompts)
             scores = [r.score for r in results]