Update app.py

app.py

@@ -3,143 +3,35 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel, VisitWebpageTool, Tool, HfApiModel, ToolCallingAgent
-import io
+from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-class AttachmentDownloadTool(Tool):
-    name = "attachment-downloader"
-    description = "Downloads the file associated with the given task_id. If it does not exist, return None. input: task_id。output: attachment files or None"
-    inputs = {
-        "task_id": {
-            "type": "str",
-            "description": "task_id that needs to download attachment files."
-        }
-    }
-    output_type = io.BytesIO
-
-    def forward(self, task_id):
-        download_url = f"{api_url}/files/"
-        
-        try:
-            response = requests.get(download_url + task_id, stream=True, timeout=15)
-            if response.status_code != 200:
-                return None
-            file_obj = io.BytesIO(response.content)
-            file_obj.seek(0)
-            return file_obj
-        except Exception as e:
-            return None
-
-class ImageCaptionTool(Tool):
-    name = "image-captioner"
-    description = "Identify the content of the input image and describe it in natural language. Input: image. Output: description text."
-    inputs = {
-        "image": {
-            "type": "image",
-            "description": "Images that need to be identified and described"
-        }
-    }
-    output_type = str
-
-    def setup(self):
-        self.model = OpenAIServerModel(
-            model_id="Qwen/Qwen2.5-VL-32B-Instruct",
-            api_base="https://api.siliconflow.cn/v1/",
-            api_key=os.getenv('MODEL_TOKEN'),
-        )
-
-    def forward(self, image):
-        prompt = "Please describe the content of this picture in detail."
-        return self.model(prompt, images=[image])
-
-class AudioToTextTool(Tool):
-    name = "audio-to-text"
-    description = "Convert the input audio content to text. Input: audio. Output: text."
-    inputs = {
-        "audio": {
-            "type": "audio",
-            "description": "The audio file that needs to be transcribed"
-        }
-    }
-    output_type = str
-
-    def setup(self):
-        # 使用 HuggingFace Hub 上的 Whisper 大模型
-        self.model = HfApiModel(model_id="openai/whisper-large-v3")  # 或其他支持音频转写的模型
-
-    def forward(self, audio):
-        prompt = "Please transcribe this audio content into text."
-        return self.model(prompt, audios=[audio])
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-# class BasicAgent:
-#     def __init__(self):
-#         print("BasicAgent initialized.")
-#     def __call__(self, question: str) -> str:
-#         print(f"Agent received question (first 50 chars): {question[:50]}...")
-#         fixed_answer = "This is a default answer."
-#         print(f"Agent returning fixed answer: {fixed_answer}")
-#         return fixed_answer
 class BasicAgent:
-    def __init__(self):
-
-        self.think_model = OpenAIServerModel(
-            model_id="THUDM/GLM-Z1-32B-0414",
-            api_base="https://api.siliconflow.cn/v1/",
-            api_key=os.getenv('MODEL_TOKEN'),
-        )
-        self.base_model = OpenAIServerModel(
-            model_id="THUDM/GLM-4-32B-0414",
-            api_base="https://api.siliconflow.cn/v1/",
-            api_key=os.getenv('MODEL_TOKEN'),
-        )
-        # self.vision_model = OpenAIServerModel(
-        #     model_id="Qwen/Qwen2.5-VL-32B-Instruct",
-        #     api_base="https://api.siliconflow.cn/v1/",
-        #     api_key=os.getenv('MODEL_TOKEN'),
-        # )
-
-        self.tools = [AttachmentDownloadTool, ImageCaptionTool, AudioToTextTool]
-
-        web_agent = ToolCallingAgent(
-            tools=[DuckDuckGoSearchTool(), VisitWebpageTool()],
-            model=self.base_model,
-            max_steps=10,
-            name="web_search_agent",
-            description="Runs web searches for you.",
-        )
-        
+    def __init__(self, openai_key):
+        self.openai_key = openai_key
+        print("BasicAgent initialized.")
+        # Initialize the model
+        #model = HfApiModel()
+        model = OpenAIServerModel(model_id="gpt-4.1", api_key=self.openai_key)
+        # Initialize the search tool
+        search_tool = DuckDuckGoSearchTool()
+        # Initialize Agent
         self.agent = CodeAgent(
-            tools=self.tools,
-            model=self.think_model,
-            managed_agents=[web_agent,],
-            additional_authorized_imports=["time", "numpy", "pandas"],
-            max_steps=20
+            model = model,
+            tools=[search_tool]
         )
-        print("BasicAgent initialized.")
-
-    def __call__(self, question: str, images=None) -> str:
-        
+    def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        
-        try:
-            if images is not None:
-                result = self.agent.run(question, images=images)
-            else:
-                result = self.agent.run(question)
-            
-            print(f"Agent returning answer: {result}")
-            return result
-        except Exception as e:
-            print(f"Agent error: {e}")
-            return f"AGENT ERROR: {e}"
+        fixed_answer =self.agent.run(question)
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+def run_and_submit_all(profile: gr.OAuthProfile | None, openai_key: str):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
@@ -160,7 +52,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = BasicAgent(openai_key)
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -268,24 +160,27 @@ with gr.Blocks() as demo:
         **Instructions:**
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
-        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        3.  Enter your OpenAI key below (if required by your agent).
+        4.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
         ---
         **Disclaimers:**
-        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
-        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        Once clicking on the "submit" button, it can take quite some time (this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance, for the delay process of the submit button, a solution could be to cache the answers and submit in a separate action or even to answer the questions in async.
         """
     )
 
     gr.LoginButton()
 
+    openai_key_box = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...", lines=1)
+
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
         fn=run_and_submit_all,
+        inputs=[openai_key_box],
         outputs=[status_output, results_table]
     )