Update README.md

README.md

@@ -1,4 +1,7 @@
 ---
+license: cc-by-4.0
+---
+---
 dataset_info:
   features:
   - name: question_id
@@ -58,15 +61,17 @@ You can find our dataset on huggingface: 🤗[InfoChartQA Dataset](https://huggi
 Each question entry is arranged as:
 
 ```
---question_id: int
---qtype: int
---figure_path: image
---visual_figure_path: list of image
---question: str
---answer: str
---instructions: str
---prompt: str
---options: list of dict ("A/B/C/D":"option_content")
+        "question_id": id of the question,
+        "qtype": type of the question, for example: "rank" questions 
+        "figure_path": local path of the image if you download the image,
+        "question": question,  
+        "answer": answer,
+        "instructions": instructions,
+        "url": url of the image,
+        "bbox": bbox of the image, this is used for visual questions. Images with bbox should be cropped based on the bbox. bbox is formatted as [xmin, ymin, width, height] with no normalization.
+        "parent": the original image of the cropped image, 
+        "difficulty": difficulty level,
+        "chart_type": chart_type,
 ```
 
 Each question is built as:
@@ -97,19 +102,25 @@ def build_question(query):#to build the question
 with open("visual_basic.json","r",encode="utf-8") as f:
 	queries = json.load(f)
 
-for idx in range(queries):
-    question = build_question(queries[idx])
-    figure_path = [queries[idx]['figure_path']]
-    visual_figure_path = queries[idx]['visual_figure_path']
-    
-	response = model.generate(question, [figure_path, visual_figure_path])# generate model's response based on 
-    
-    queries[idx]["response"] = reponse
-
-with open("model_reponse.json","w",encode="utf-8") as f:
-	json.dump(queries, f)
-from checker import evaluate
-evaluate("model_reponse.json", "path_to_save_the_result")
+for query in tqdm(queries):
+    query_idx = query["question_id"]
+    question_text = build_question(query)
+    chart_figure = query["url"]  # This should be a list of url
+    visual_figure = query.get("visual_figure_path",[])
+
+    # Replace with your model
+    response = model.generate(question_text, chart_figure + visual_figure)
+
+
+    Responses[query_idx] = {
+        "qtype": int(query["qtype"]),
+        "answer": query["answer"],
+        "question_id": query_idx,
+        "response": response,
+    }
+
+with open("./model_response.json", "w", encoding="utf-8") as f:
+    json.dump(Responses, f, indent = 2, ensure_ascii=False)
 ```