Upload test.py

test.py

@@ -0,0 +1,183 @@
+import anthropic
+import base64
+import httpx
+import os
+import time
+from mimetypes import guess_type
+
+import random
+# import numpy as np
+def seed_everything(seed):
+    random.seed(seed)
+    # np.random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    # torch.manual_seed(seed)
+    # torch.cuda.manual_seed(seed)
+    # torch.backends.cudnn.deterministic = True
+    # env.seed(seed)
+seed_everything(1)
+
+def local_image_to_data_url(image_path):
+    mime_type, _ = guess_type(image_path)
+    if mime_type is None:
+        mime_type = 'application/octet-stream'
+    with open(image_path, "rb") as image_file:
+        base64_encoded_data = base64.b64encode(image_file.read()).decode('utf-8')
+    return base64_encoded_data
+
+client = anthropic.Anthropic(
+    api_key="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",
+)
+
+
+# Start test
+levels = [3,4,5,6,7,8]
+in_context_example_num = 0 # 0, 1, 2, 4, 8
+
+if in_context_example_num > 0:
+    output_path = "output/output_img_%d/"%(in_context_example_num)
+    input_backup_path = "input/input_backup_img_%d/"%(in_context_example_num)
+else:
+    output_path = "output/output_img/"
+    input_backup_path = "input/input_backup_img/"
+
+os.makedirs(output_path, exist_ok=True)
+os.makedirs(input_backup_path, exist_ok=True)
+
+EXAMPLE_DICT = {
+    3: [],
+    4: [],
+    5: [],
+    6: [],
+    7: [],
+    8: [],
+}
+# for level in levels:
+#     for example_id in range(8):
+#         curr_example_pack = {}
+#         curr_example_pack["image_path"] = "../example/level%d/img/%d.png"%(level, example_id)
+#         with open("../example/level%d/answer/%d.txt"%(level, example_id), "r") as f:
+#             curr_example_pack["answer"] = f.read()
+        
+        # curr_example_pack["pure_text"] = "../example/level%d/pure_text/%d.txt"%(level, example_id)
+        # curr_example_pack["table"] = "../example/level%d/table/%d.txt"%(level, example_id)
+
+        # curr_example_pack["start_image_path"] = "../example/level%d/begin/%d.jpg"%(level, example_id)
+        # curr_example_pack["end_image_path"] = "../example/level%d/end/%d.jpg"%(level, example_id)
+        # example_path = "../example/level%d/"%(level)
+        # curr_example_pack["question1"] = "\n\nPlease generate the moving plan. The beginning state is:"
+        # curr_example_pack["question2"] = "\nThe end state is:"
+        # with open(example_path + "sol_%d.txt"%(example_id), "r") as f:
+        #     curr_example_pack["answer"] = f.read()
+        # EXAMPLE_DICT[level].append(curr_example_pack)
+
+
+import ipdb; ipdb.set_trace()
+for level in levels:
+    os.makedirs(output_path + "level%d"%(level), exist_ok=True)
+    os.makedirs(input_backup_path + "level%d"%(level), exist_ok=True)
+    start_idx = 0
+    end_idx = 100
+    runned_term = 0
+    map_path = "../maps/level%d/img/"%(level)
+    while True:
+        try:
+            curr_id = start_idx + runned_term
+            if curr_id >= end_idx:
+                break
+            
+            prompt_input_1 = '''            
+In this task, you will analyze a maze to determine if there is a hole in a specific position.
+The following figure illustrates the appearances of the player, holes, lands, and the goal within the maze. You will need to focus on the appearance of the hole.
+'''
+            prompt_input_2 = '''
+Here is an example to illustrate how to analyze and answer the question:
+'''
+            prompt_input_3 = '''
+Example question: Is there a hole in row 3, column 3?
+
+In this example:
+- We check the position in row 3, column 3.
+- According to the image, it is a land square. It does not contain a hole.
+- Therefore, you will output "<Output> No".
+
+Your output should be: "<Output> No" or "<Output> Yes", depending on whether there is a hole at the specified position.
+'''
+            # prompt_examples = []
+            # image_examples = []
+            # if in_context_example_num > 0:
+            #     prompt_examples.append("## Example:\n")
+            #     example_indices = random.sample(range(8), in_context_example_num)
+            #     for example_index in example_indices:
+            #         this_example = EXAMPLE_DICT[level][example_index]
+            #         image_examples.append(local_image_to_data_url(this_example["image_path"]))
+            #         prompt_examples.append(this_example["answer"])
+            prompt_input_4 = "\n\nNow you will analyze the following maze and answer the question: "
+            with open("../maps/level%d/question/%d.txt"%(level, curr_id), "r") as f:
+                prompt_input_5 = f.read()
+
+            # construct 
+            content_input_seq = []
+            content_input_seq.append({ 
+                            "type": "text", 
+                            "text": prompt_input_1,
+                        })
+            content_input_seq.append({ 
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": "image/png",
+                                "data": local_image_to_data_url("../prompt-visual-images/system-figure-1.png"),
+                            }
+                        })
+            content_input_seq.append({ 
+                            "type": "text", 
+                            "text": prompt_input_2,
+                        })
+            content_input_seq.append({ 
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": "image/png",
+                                "data": local_image_to_data_url("../prompt-visual-images/system-figure-2.png"),
+                            }
+                        })
+            content_input_seq.append({ 
+                            "type": "text", 
+                            "text": prompt_input_3,
+                        })
+            content_input_seq.append({
+                "type": "text",
+                "text": prompt_input_4,
+            })
+            content_input_seq.append({ 
+                "type": "image",
+                "source": {
+                    "type": "base64",
+                    "media_type": "image/png",
+                    "data": local_image_to_data_url(map_path + "%d.png"%(curr_id)),
+                }
+            })
+            content_input_seq.append({
+                "type": "text",
+                "text": prompt_input_5,
+            })
+            response = client.messages.create(
+                model="claude-3-sonnet-20240229",
+                max_tokens=1024,
+                system="You are a maze-solving agent playing a pixelated maze videogame.\nMazes are presented on grid maps, where each tile can be empty land, or contain a player, hole, or goal.",
+                messages=[
+                    { 
+                        "role": "user", 
+                        "content": content_input_seq
+                    },
+                ],
+            )
+            with open(output_path + "level%d/%d.txt"%(level, curr_id), "w") as f:
+                f.write(response.content[0].text)
+            time.sleep(2)
+            runned_term += 1
+        except:
+            time.sleep(2)
+            pass
+