Update app.py

app.py

@@ -1,169 +1,46 @@
 import os
+import gradio as gr
 import requests
+import inspect
 import pandas as pd
-import gradio as gr
-import tempfile
-import json
-from pathlib import Path
-from typing import Union, Optional
-from smolagents import LiteLLMModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool
-from smolagents.tools import Tool
-
-
-# --- Function to Configure Google Credentials (ESSENTIAL) ---
-def setup_google_credentials():
-    """
-    Reads Google Cloud credential JSON content from an environment variable,
-    writes it to a temporary file, and sets the GOOGLE_APPLICATION_CREDENTIALS
-    environment variable to the path of that file.
-
-    This function should be called before any Google Cloud client library
-    (like the one used by LiteLLM for Vertex AI) is initialized.
-
-    Requires the service account key JSON content to be stored in an
-    environment variable named 'GOOGLE_APPLICATION_CREDENTIALS_JSON'.
-    Set this in your Hugging Face Space secrets.
-    """
-    credentials_json_str = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS_JSON")
-    if not credentials_json_str:
-        print("ERROR: 'GOOGLE_APPLICATION_CREDENTIALS_JSON' secret not found in environment variables.")
-        print("       Please ensure you have set this secret in your Hugging Face Space settings.")
-        # Depending on requirements, you might want to raise an error here
-        # raise ValueError("Secret 'GOOGLE_APPLICATION_CREDENTIALS_JSON' not set.")
-        return False # Indicate failure
-
-    try:
-        # Create a secure temporary file to store the credentials
-        # delete=False ensures the file persists until the process exits or it's manually cleaned up.
-        # We need the file path to set the environment variable.
-        with tempfile.NamedTemporaryFile(mode='w', suffix=".json", delete=False, encoding='utf-8') as temp_f:
-            temp_f.write(credentials_json_str)
-            credentials_path = temp_f.name # Get the path to the temporary file
-
-        # Set the environment variable that Google client libraries expect
-        os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credentials_path
-        print(f"Google Application Credentials successfully set to temporary file: {credentials_path}")
-        return True # Indicate success
-    except json.JSONDecodeError:
-        print("ERROR: Failed to parse the content of 'GOOGLE_APPLICATION_CREDENTIALS_JSON'. Ensure it's valid JSON.")
-        return False
-    except OSError as e:
-        print(f"ERROR: Failed to write credentials to temporary file: {e}")
-        return False
-    except Exception as e:
-        print(f"ERROR: An unexpected error occurred during Google credential setup: {e}")
-        # You might want to re-raise the exception depending on your error handling strategy
-        # raise e
-        return False
-
-# --- Call Credential Setup EARLY ---
-# This needs to run before any code (like BasicAgent initialization) tries to use Google Cloud services.
-print("Attempting to configure Google Cloud credentials...")
-CREDENTIALS_CONFIGURED = setup_google_credentials()
-if not CREDENTIALS_CONFIGURED:
-     print("WARNING: Google Cloud credentials setup failed. Agent initialization might fail.")
 
+from smolagents import (
+    ToolCallingAgent, 
+    CodeAgent,
+    DuckDuckGoSearchTool, 
+    InferenceClientModel, 
+    HfApiModel, 
+    OpenAIServerModel
+)
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-
-### Defining tools ###
-
-class ExcelToTextTool(Tool):
-    """Render an Excel worksheet as Markdown text."""
-
-    name = "excel_to_text"
-    description = (
-        "Read an Excel file and return a Markdown table of the requested sheet. "
-        "Accepts either the sheet name or the zero-based index."
-    )
-
-    inputs = {
-        "excel_path": {
-            "type": "string",
-            "description": "Path to the Excel file (.xlsx / .xls).",
-        },
-        "sheet_name": {
-            "type": "string",
-            "description": (
-                "Worksheet name or zero‑based index *as a string* (optional; default first sheet)."
-            ),
-            "nullable": True,
-        },
-    }
-
-    output_type = "string"
-
-    def forward(
-            self,
-            excel_path: str,
-            sheet_name: Optional[str] = None,
-    ) -> str:
-        """Load *excel_path* and return the sheet as a Markdown table."""
-
-        path = Path(excel_path).expanduser().resolve()
-        if not path.exists():
-            return f"Error: Excel file not found at {path}"
-
-        try:
-            # Interpret sheet identifier
-            sheet: Union[str, int]
-            if sheet_name is None or sheet_name == "":
-                sheet = 0 # first sheet
-            else:
-                # If the user passed a numeric string (e.g. "1"), cast to int
-                sheet = int(sheet_name) if sheet_name.isdigit() else sheet_name
-
-            # Load worksheet
-            df = pd.read_excel(path, sheet_name=sheet)
-
-            # Render to Markdown, fallback to tabulate if needed
-            if hasattr(pd.DataFrame, "to_markdown"):
-                return df.to_markdown(index=False)
-            from tabulate import tabulate
-
-            return tabulate(df, headers="keys", tablefmt="github", showindex=False)
-
-        except Exception as exc:  # broad catch keeps the agent chat‑friendly
-            return f"Error reading Excel file: {exc}"
-
-
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        # 检查DeepSeek API密钥
-        deepseek_api_key = os.environ.get("DEEPSEEK_API_KEY")
-        if not deepseek_api_key:
-            raise ValueError("DEEPSEEK_API_KEY environment variable not set.")
-
-        
-        # Assuming you've set GOOGLE_API_KEY in secrets
-        google_api_key = os.environ.get("GOOGLE_API_KEY")
-        if not google_api_key:
-           raise ValueError("GOOGLE_API_KEY environment variable not set.")
+        self.model = OpenAIServerModel(
+            model_id='deepseek-chat',
+            api_base="https://api.deepseek.com",
+            api_key=os.environ["DEEPSEEK_API_KEY"],
+        )
+        self.agent = ToolCallingAgent(
+            tools=[DuckDuckGoSearchTool()], 
+            model=self.model,
+            add_base_tools=True
+        )
+        #self.agent.prompt_templates['system_prompt'] = """
 
-        # # Check if the global credential setup was successful
-        # if not CREDENTIALS_CONFIGURED:
-        #      raise ValueError("Google Cloud credentials could not be configured. Check startup logs and HF Secrets (ensure 'GOOGLE_APPLICATION_CREDENTIALS_JSON' is set correctly).")
+        #You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
 
-        # 设置环境变量，使LiteLLM能够使用DeepSeek API密钥
-        os.environ["DEEPSEEK_API_KEY"] = deepseek_api_key
-            
-        self.agent = CodeAgent(
-            model=LiteLLMModel(model_id="deepseek-chat"),
-            tools=[DuckDuckGoSearchTool(), WikipediaSearchTool(), ExcelToTextTool()],
-            add_base_tools=True,
-            additional_authorized_imports=['pandas','numpy','csv','subprocess']
-        )
+        #"""
         print("BasicAgent initialized.")
-
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
         fixed_answer = self.agent.run(question)
-        print(f"Agent returning answer: {fixed_answer}")
+        print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
@@ -220,7 +97,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    for i, item in enumerate(questions_data):
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
@@ -233,6 +110,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         except Exception as e:
              print(f"Error running agent on task {task_id}: {e}")
              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+        
 
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
@@ -340,4 +218,4 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)