Added file reading

app.py

@@ -2,6 +2,7 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+import json
 from phoenix.otel import register
 from openinference.instrumentation.smolagents import SmolagentsInstrumentor
 from llm_only_agent import LLMOnlyAgent
@@ -40,6 +41,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None, limit: int | None):
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
+    files_url = f"{api_url}/files"
     submit_url = f"{api_url}/submit"
 
     # 1. Instantiate Agent ( modify this part to create your agent)
@@ -53,37 +55,51 @@ def run_and_submit_all( profile: gr.OAuthProfile | None, limit: int | None):
     print(agent_code)
 
     # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-             print("Fetched questions list is empty.")
-             return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-         print(f"Error decoding JSON response from questions endpoint: {e}")
-         print(f"Response text: {response.text[:500]}")
-         return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+    # Fetch questions locally in test mode
+    task_ids = []
+    if task_ids:
+        print("Fetching questions from local file")
+        with open("data/questions.json", "r") as f:
+            questions_data = json.load(f)
+            questions_data = [q for q in questions_data if q['task_id'] in task_ids]
+    # Otherwise fetch from Hugging Face API
+    else:
+        print(f"Fetching questions from: {questions_url}")
+        try:
+            response = requests.get(questions_url, timeout=15)
+            response.raise_for_status()
+            questions_data = response.json()
+            if not questions_data:
+                print("Fetched questions list is empty.")
+                return "Fetched questions list is empty or invalid format.", None
+            print(f"Fetched {len(questions_data)} questions.")
+        except requests.exceptions.RequestException as e:
+            print(f"Error fetching questions: {e}")
+            return f"Error fetching questions: {e}", None
+        except requests.exceptions.JSONDecodeError as e:
+            print(f"Error decoding JSON response from questions endpoint: {e}")
+            print(f"Response text: {response.text[:500]}")
+            return f"Error decoding server response for questions: {e}", None
+        except Exception as e:
+            print(f"An unexpected error occurred fetching questions: {e}")
+            return f"An unexpected error occurred fetching questions: {e}", None
 
     # 3. Run your Agent
     results_log = []
     answers_payload = []
     # Limit for test purposes
-    limit = 2
+    limit = None
     if limit is not None:
         questions_data = questions_data[:limit]
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
-        question_text = item.get("question")
+        file_name = item.get("file_name")
+        if file_name != "":
+           file_path = f"{files_url}/{task_id}"
+           question_text = item.get("question") + "The mentionned file can be downloaded from the following link: " + file_path
+        else:
+           question_text = item.get("question")
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue

multi_agent.py

@@ -1,4 +1,3 @@
-from pydoc import describe
 import re
 from smolagents import AgentMemory, CodeAgent, InferenceClientModel, FinalAnswerTool, WebSearchTool
 from collections.abc import Callable
@@ -17,7 +16,7 @@ class MultiAgent:
 
         # WEB AGENT
         self.web_agent = CodeAgent(
-            model=InferenceClientModel("Qwen/Qwen2.5-Coder-32B-Instruct", provider="together", max_tokens=8096),
+            model=InferenceClientModel("Qwen/Qwen2.5-Coder-32B-Instruct", provider="nebius", max_tokens=8096),
             tools=[WikipediaSearchTool(), WebSearchTool(), VisitWebpageTool()],
             name="web_agent",
             description="A code agent that can search the web and visit webpages",
@@ -27,13 +26,14 @@ class MultiAgent:
 
         # MANAGER AGENT
         self.manager_agent = CodeAgent(
-            model=InferenceClientModel("deepseek-ai/DeepSeek-R1", provider="together", max_tokens=8096),
+            model=InferenceClientModel("deepseek-ai/DeepSeek-R1", provider="nebius", max_tokens=8096),
             tools=[FinalAnswerTool()],
             managed_agents=[self.web_agent],
             instructions=self.instructions,
             additional_authorized_imports=[
                 "pandas",
                 "numpy",
+                "chess"
             ],
             planning_interval=5,
             verbosity_level=2,

requirements.txt

@@ -7,3 +7,4 @@ wikipedia-api
 markdownify
 requests
 smolagents[telemetry,toolkit]
+chess