evals moved + readme

environments/eval_environments/vision_evals/realworldqa_environment.py

@@ -0,0 +1,119 @@
+import asyncio
+import base64
+import io
+from typing import List, Tuple
+
+from datasets import load_dataset
+from PIL import Image
+
+from atroposlib.envs.server_handling.server_manager import ServerManager
+from environments.eval_environments.eval import EvalBase, eval_runner
+
+
+class RealWorldQA(EvalBase):
+    def setup_data(self) -> list:
+        split = getattr(self, "split", "test")
+        dataset = load_dataset("xai-org/RealworldQA", split=split)
+        print(f"Loaded {len(dataset)} examples from RealWorldQA ({split})")
+        return list(dataset)
+
+    def encode_image(self, pil_image: Image.Image) -> str:
+        buffer = io.BytesIO()
+        pil_image.save(buffer, format="PNG")
+        return base64.b64encode(buffer.getvalue()).decode("utf-8")
+
+    def get_image_base64(self, item: dict) -> str:
+        if "image" in item and item["image"] is not None:
+            if isinstance(item["image"], Image.Image):
+                return self.encode_image(item["image"])
+        raise ValueError("Could not find image for item")
+
+    def build_messages(self, item: dict) -> List[dict]:
+        image_base64 = self.get_image_base64(item)
+        question = item.get("question", "")
+
+        prompt = f"""{question}
+
+Provide a brief, direct answer."""
+
+        return [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/png;base64,{image_base64}"},
+                    },
+                    {"type": "text", "text": prompt},
+                ],
+            }
+        ]
+
+    def extract_answer(self, response: str) -> str:
+        response = response.strip()
+        lines = response.split("\n")
+        if lines:
+            return lines[0].strip()
+        return response
+
+    def score(self, prediction: str, answer: str) -> bool:
+        pred = prediction.strip().lower()
+        ans = answer.strip().lower()
+
+        if not pred:
+            return False
+
+        if pred == ans:
+            return True
+
+        if ans in pred or pred in ans:
+            return True
+
+        pred_words = set(pred.split())
+        ans_words = set(ans.split())
+        overlap = pred_words & ans_words
+        if len(overlap) >= len(ans_words) * 0.5:
+            return True
+
+        return False
+
+    async def run_item(self, server: ServerManager, data_item: dict) -> Tuple[dict, dict]:
+        try:
+            messages = self.build_messages(data_item)
+
+            completion = await self.chat_completion(server, messages)
+
+            if not completion.choices:
+                return {"accuracy": 0.0}, {"error": "Empty response"}
+
+            message = completion.choices[0].message
+            response = message.content or ""
+            if hasattr(message, "reasoning") and message.reasoning and not response:
+                response = message.reasoning
+            if not response and hasattr(message, "model_extra"):
+                reasoning = message.model_extra.get("reasoning", "")
+                if reasoning:
+                    response = reasoning
+
+            if not response:
+                return {"accuracy": 0.0}, {"error": "Empty response"}
+
+            extracted = self.extract_answer(response)
+            answer = data_item.get("answer", "")
+            correct = self.score(extracted, answer)
+
+            sample = {
+                "question": data_item.get("question", ""),
+                "answer": answer,
+                "prediction": extracted,
+                "correct": correct,
+            }
+
+            return {"accuracy": 1.0 if correct else 0.0}, sample
+
+        except Exception as e:
+            return {"accuracy": 0.0}, {"error": str(e)}
+
+
+if __name__ == "__main__":
+    asyncio.run(eval_runner(RealWorldQA(split="test", temperature=0.0, max_tokens=256)))