Method predict_single

AgentBench.old/src/tasks/mind2web/task.py:145–186

(self, session: Session, sample: Dict)

        return ret          

    def predict_single(self, session: Session, sample: Dict): 
        if len(sample["pos_ids"]) == 0:
            return {"final_prediction":  ('', ''), "outputs": []}
        pos_ids = sample["pos_ids"]        
        neg_candidates = sample["neg_candidates"]
        neg_candidates = [c for c in neg_candidates if c["rank"] < self.top_k]
        neg_ids = [c["backend_node_id"] for c in neg_candidates]
        all_candidates = pos_ids + neg_ids
        random.shuffle(all_candidates)
        final_prediction = None
        outputs = []
        while len(all_candidates) > 1:
            candidate_ids = all_candidates[:self.candidates_num] # 5
            all_candidates = all_candidates[self.candidates_num:]
            seq_context, seq_in, _, choices = format_input_multichoice(
                sample, candidate_ids, -1, keep_html_brackets=True
            )
            outputs.append(
                [candidate_ids, [seq_context, seq_in, choices], None]
            )
            self.prompt_template[-1][
                    "content"
                ] = f"'''\n{seq_context}\n'''\n\n{seq_in}"
            
            session.history = []
            output = fetch_data(session, self.prompt_template)
            # print(session.history[-1])
            # output = "CLICK "
            outputs[-1][-1] = output
            pred_element, pred_action = self.postprocess_action_llm(output)
            if pred_element != "A":
                # convert B, C, D to 0, 1, 2
                pred_element = ord(pred_element) - ord("B")
                try:
                    pred_element = choices[pred_element][0]
                    all_candidates.append(pred_element)
                    final_prediction = (pred_element, pred_action)
                except IndexError:
                    print(f"IndexError: {output}")
        if final_prediction == None or len(all_candidates) == 0:
            final_prediction = ('', '')
        return {"final_prediction": final_prediction, "outputs": outputs}
    
    def postprocess_action(self, text):
        # C.