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diff --git a/data_utils/chart/data_collector.py b/data_utils/chart/data_collector.py
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+++ b/data_utils/chart/data_collector.py
@@ -0,0 +1,140 @@
+import os
+import json
+from typing import List, Dict, Any
+from config import CONFIG
+from data_utils.paths import resolve_image_path
+from data_utils.rl_prompt import PROMPT_TEMPLATE
+ANSWER_TEMPLATE = CONFIG['rl']['answer_flag'] + " " +  "{answer}"
+
+def prepare_chart_rl_data(json_path: str) -> List[Dict[str, Any]]:
+    """
+    Processes a JSON file of chart data for Reinforcement Learning.
+
+    This function reads a JSON file, filters out entries marked as 'machine-generated',
+    cleans the 'answer' field, and constructs a formatted 'prompt'.
+
+    Args:
+        json_path: The file path to the input JSON data.
+
+    Returns:
+        A list of processed dictionaries, each with a new 'prompt' key.
+
+    Raises:
+        FileNotFoundError: If the json_path does not exist.
+    """
+    # Use a clear check for file existence and raise a specific error.
+    if not os.path.exists(json_path):
+        raise FileNotFoundError(f"Error: The file '{json_path}' was not found.")
+
+    # Use 'with open' for safe file handling.
+    with open(json_path, 'r', encoding='utf-8') as f:
+        raw_data = json.load(f)
+
+    processed_data = []
+    # Use a single, clear loop to both filter and process the data.
+    for entry in raw_data:
+        # Filter condition: Keep if the key is missing or its value is 0 (human).
+        if entry.get('human_or_machine', 0) == 0:
+            # Create a new dictionary to avoid modifying the original list in place.
+            new_entry = entry.copy()
+
+            # Clean up the answer text.
+            if 'answer' in new_entry:
+                new_entry['answer'] = ANSWER_TEMPLATE.format(answer=new_entry['answer'].strip())
+
+            image = new_entry.get('image', '')
+            if image:
+                new_entry['image'] = resolve_image_path(image)
+            # Format the prompt using an f-string.
+            new_entry['prompt'] = PROMPT_TEMPLATE.format(question=new_entry['question'])
+            new_entry['question_wo_prompt'] = new_entry['question']
+            # Preserve optional privileged-context fields for OPSD
+            if 'visual_fact' not in new_entry and 'visual_facts' in new_entry:
+                new_entry['visual_fact'] = new_entry['visual_facts']
+            for priv_key in ('visual_fact_hint', 'visual_fact_deplot', 'evidence_bbox', 'hint'):
+                if priv_key in entry and priv_key not in new_entry:
+                    new_entry[priv_key] = entry[priv_key]
+            # Optionally remove the 'human_or_machine' key from the final output.
+            new_entry.pop('human_or_machine', None)
+            new_entry.pop('question', None)
+
+            processed_data.append(new_entry)
+
+    return processed_data
+
+
+def prepare_chart_sft_data(json_path: str) -> List[Dict[str, Any]]:
+    """
+    Processes a JSON file of chart data for Reinforcement Learning.
+
+    This function reads a JSON file, filters out entries marked as 'machine-generated',
+    cleans the 'answer' field, and constructs a formatted 'prompt'.
+
+    Args:
+        json_path: The file path to the input JSON data.
+
+    Returns:
+        A list of processed dictionaries, each with a new 'prompt' key.
+
+    Raises:
+        FileNotFoundError: If the json_path does not exist.
+    """
+    # Use a clear check for file existence and raise a specific error.
+    if not os.path.exists(json_path):
+        raise FileNotFoundError(f"Error: The file '{json_path}' was not found.")
+
+    # Use 'with open' for safe file handling.
+    with open(json_path, 'r', encoding='utf-8') as f:
+        raw_data = json.load(f)
+
+    processed_data = []
+    # Use a single, clear loop to both filter and process the data.
+    for entry in raw_data:
+        # Filter condition: Keep if the key is missing or its value is 0 (human).
+        if entry.get('human_or_machine', 0) == 0:
+            # Create a new dictionary to avoid modifying the original list in place.
+            new_entry = entry.copy()
+
+            # Clean up the answer text.
+            if 'answer' in new_entry:
+                new_entry['answer'] = new_entry['hint'] + '\n' + ANSWER_TEMPLATE.format(answer=new_entry['answer'].strip())
+
+            # Format the prompt using an f-string.
+            new_entry['prompt'] = PROMPT_TEMPLATE.format(question=new_entry['question'])
+            new_entry['question_wo_prompt'] = new_entry['question']
+            new_entry.pop('question', None)
+            # Optionally remove the 'human_or_machine' key from the final output.
+            new_entry.pop('human_or_machine', None)
+
+            processed_data.append(new_entry)
+
+    return processed_data
+
+# --- Example of How to Use ---
+if __name__ == "__main__":
+    dummy_data = [
+        {"question": "What was the trend in 2022?", "answer": " The trend was upward. ", "human_or_machine": 0},
+        {"question": "Which category was highest?", "answer": "Category A was highest.", "human_or_machine": 1},
+        {"question": "Summarize the chart.", "answer": " It shows growth. "}
+    ]
+    dummy_filepath = "sample_chart_data.json"
+    with open(dummy_filepath, "w") as f:
+        json.dump(dummy_data, f, indent=2)
+
+    # Process the data using the refactored function.
+    try:
+        final_data = prepare_chart_rl_data(dummy_filepath)
+
+        # Pretty-print the output.
+        print(json.dumps(final_data, indent=2))
+
+        # Expected output:
+        # The entry with "human_or_machine": 1 will be filtered out.
+        # The other two entries will be processed with a new 'prompt' key.
+
+    except FileNotFoundError as e:
+        print(e)
+    finally:
+        # Clean up the dummy file.
+        if os.path.exists(dummy_filepath):
+            os.remove(dummy_filepath)
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