First commit

app.py

@@ -1,8 +1,13 @@
 import os
 import gradio as gr
-import requests
 import inspect
 import pandas as pd
+import importlib
+from importlib import resources
+import requests
+import yaml
+import numpy as np
+from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool, WikipediaSearchTool, Tool, OpenAIServerModel, SpeechToTextTool
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -10,14 +15,76 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+
+class GetTaskFileTool(Tool):
+    name = "get_task_file_tool"
+    description = """This tool downloads the file content associated with the given task_id if exists. Returns absolute file path"""
+    inputs = {
+        "task_id": {"type": "string", "description": "Task id"},
+        "file_name": {"type": "string", "description": "File name"},
+    }
+    output_type = "string"
+
+    def forward(self, task_id: str, file_name: str) -> str:
+        response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}", timeout=15)
+        response.raise_for_status()
+        with open(file_name, 'wb') as file:
+            file.write(response.content)
+        return os.path.abspath(file_name)
+
+class LoadXlsxFileTool(Tool):
+    name = "load_xlsx_file_tool"
+    description = """This tool loads xlsx file into pandas and returns it"""
+    inputs = {
+        "file_path": {"type": "string", "description": "File path"}
+    }
+    output_type = "object"
+
+    def forward(self, file_path: str) -> object:
+        return pd.read_excel(file_path)
+
+class LoadTextFileTool(Tool):
+    name = "load_text_file_tool"
+    description = """This tool loads any text file"""
+    inputs = {
+        "file_path": {"type": "string", "description": "File path"}
+    }
+    output_type = "string"
+
+    def forward(self, file_path: str) -> object:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            return file.read()
+
+prompts = yaml.safe_load(
+    resources.files("smolagents.prompts").joinpath("code_agent.yaml").read_text()
+)
+
+prompts["system_prompt"] = ("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. "
+                            + prompts["system_prompt"])
+
+class DeepseekAgent:
     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
+        self.__agent = CodeAgent(
+            tools=[
+                DuckDuckGoSearchTool(),
+                VisitWebpageTool(),
+                WikipediaSearchTool(),
+                GetTaskFileTool(),
+                SpeechToTextTool(),
+                LoadXlsxFileTool(),
+                LoadTextFileTool()
+            ],
+            model=OpenAIServerModel(
+                model_id="deepseek-ai/DeepSeek-R1-0528",
+                api_base="https://llm.chutes.ai/v1",
+                api_key=os.getenv("CHUTES_API_KEY"),
+                temperature=0.6),
+            prompt_templates=prompts,
+            max_steps=15,
+            additional_authorized_imports = ["pandas"]
+        )
+    def ask_question(self, question):
+        return self.__agent.run(question)
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +107,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = DeepseekAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -76,11 +143,18 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
+        print(question_text)
+        file_name = item.get("file_name")
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = agent.ask_question(f"Task id: {task_id}. Task file: {file_name if file_name != '' else 'is absent'}. Task: " + question_text)
+            if isinstance(submitted_answer, (np.integer, np.floating)):
+                submitted_answer = submitted_answer.item()  # Convert NumPy types to Python native types
+            elif isinstance(submitted_answer, list):
+                submitted_answer = [x.item() if isinstance(x, (np.integer, np.floating)) else x for x in submitted_answer]
+            submitted_answer = str(submitted_answer)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:
@@ -142,21 +216,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    gr.Markdown(
-        """
-        **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.
-
-        ---
-        **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.
-        """
-    )
+    gr.Markdown("# Final Assignment: Deepseek Agent, which undergoes certain testing")
 
     gr.LoginButton()
 
@@ -193,4 +253,4 @@ 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)