Add basic Camel example

Author: anjieyang

examples/camel_basic.py

@@ -0,0 +1,96 @@
+# =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =========== Copyright 2024 @ CAMEL-AI.org. All Rights Reserved. ===========
+from termcolor import colored
+
+from camel.societies import RolePlaying
+from camel.utils import print_text_animated
+
+from crab import Benchmark, create_benchmark
+from crab.agents.backend_models import OpenAIModel
+from crab.agents.policies import SingleAgentPolicy
+from crab.benchmarks.template import template_benchmark_config
+
+def camel_task_generator():
+    task_prompt = "Design a custom game using pygame"
+    print(colored(f"Original task prompt:\n{task_prompt}\n", "yellow"))
+    role_play_session = RolePlaying("Computer Programmer", "Gamer", task_prompt=task_prompt)
+    print(colored(f"Specified task prompt:\n{role_play_session.task_prompt}\n", "cyan"))
+
+    chat_turn_limit, n = 50, 0
+    input_msg = role_play_session.init_chat()
+    while n < chat_turn_limit:
+        n += 1
+        assistant_response, user_response = role_play_session.step(input_msg)
+        print_text_animated(colored(f"AI User:\n\n{user_response.msg.content}\n", "blue"))
+        print_text_animated(colored(f"AI Assistant:\n\n{assistant_response.msg.content}\n", "green"))
+
+        if "CAMEL_TASK_DONE" in user_response.msg.content:
+            break
+
+        input_msg = assistant_response.msg
+
+    return role_play_session.task_prompt
+
+def start_benchmark(benchmark: Benchmark, agent: SingleAgentPolicy):
+    for step in range(20):
+        print("=" * 40)
+        print(f"Start agent step {step}:")
+        observation = benchmark.observe()["template_env"]
+        print(f"Current environment observation: {observation}")
+        response = agent.chat(
+            {
+                "template_env": [
+                    (f"Current environment observation: {observation}", 0),
+                ]
+            }
+        )
+        print(colored(f"Agent take action: {response}", "blue"))
+
+        for action in response:
+            response = benchmark.step(
+                action=action.name,
+                parameters=action.arguments,
+                env_name=action.env,
+            )
+            print(
+                colored(
+                    f'Action "{action.name}" success, stat: '
+                    f"{response.evaluation_results}",
+                    "green",
+                )
+            )
+            if response.terminated:
+                print("=" * 40)
+                print(
+                    colored(
+                        f"Task finished, result: {response.evaluation_results}",
+                        "green"
+                    )
+                )
+                return
+
+if __name__ == "__main__":
+    task_description = camel_task_generator()
+
+    benchmark = create_benchmark(template_benchmark_config)
+    task, action_space = benchmark.start_task("0", task_description)
+    env_descriptions = benchmark.get_env_descriptions()
+
+    model = OpenAIModel(model="gpt-4o", history_messages_len=5)
+    agent = SingleAgentPolicy(model_backend=model)
+    agent.reset(task_description, action_space, env_descriptions)
+
+    print("Start performing task: " + colored(f'"{task_description}"', "green"))
+    start_benchmark(benchmark, agent)
+    benchmark.reset()