-- File downloading logic is added.

app.py

@@ -1,3 +1,4 @@
+import asyncio
 import os
 import gradio as gr
 import requests
@@ -32,6 +33,7 @@ async def run_and_submit_all( profile: gr.OAuthProfile | None):
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
+    file_url = f"{api_url}/files"
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
@@ -71,9 +73,31 @@ async 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")
+        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
+        if file_name:
+            if not os.path.exists("resource"):
+                os.makedirs("resource")
+            try:
+                download_url = f"{file_url}/{task_id}"
+                response = requests.get(download_url)
+                response.raise_for_status()
+                file_path = os.path.join("resource", file_name)
+                with open(file_path, "wb") as f:
+                    f.write(response.content)
+                print(f"Successfully downloaded {file_name} to {file_path}")
+                # Read the file content and pass it to the agent
+                with open(file_path, "r", encoding="utf-8") as f:
+                    file_content = f.read()
+                question_text = f"{question_text}\n\nFile content:\n{file_content}"
+            except requests.exceptions.RequestException as e:
+                print(f"Error downloading file {file_name}: {e}")
+            except IOError as e:
+                print(f"Error reading file {file_name}: {e}")
+
         try:
             submitted_answer = await agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
@@ -82,6 +106,9 @@ async def run_and_submit_all( profile: gr.OAuthProfile | None):
              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}"})
 
+        # Wait for a short moment to avoid overwhelming the server (optional)
+        await asyncio.sleep(1 * 60)
+
     if not answers_payload:
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
@@ -108,7 +135,7 @@ async def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
     except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
+        error_detail = f"Server responded with status {e.response.status_code}. "
         try:
             error_json = e.response.json()
             error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
@@ -139,7 +166,7 @@ async def run_and_submit_all( profile: gr.OAuthProfile | None):
 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 ...
@@ -150,7 +177,7 @@ with gr.Blocks() as demo:
         **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.LoginButton()
@@ -188,4 +215,4 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False, server_name="0.0.0.0")
+    demo.launch(debug=True, share=False, server_name="0.0.0.0")

langchain_agent.py

@@ -71,27 +71,28 @@ class LangChainAgent:
         tools = await client.get_tools()
         print(tools)
 
-        # model_name = "gemini-2.0-flash"
-        # model_provider = "google_genai" #google_genai
+        model_name = "gemini-2.0-flash"
+        model_provider = "google_genai" #google_genai
 
-        # model = init_chat_model(model_name, model_provider=model_provider)
+        model = init_chat_model(model_name, model_provider=model_provider)
 
 
-        # model_name = "deepseek-ai/deepseek-v3.1"
-        # model_name = "deepseek-ai/deepseek-v3.1-terminus"
-        # model_name = "minimaxai/minimax-m2"
-        # model_name = "mistralai/mistral-nemotron"
-        # model_name = "qwen/qwen3-next-80b-a3b-instruct"
-        model_name = "qwen/qwen3-next-80b-a3b-thinking"
-        # model_name = "moonshotai/kimi-k2-instruct-0905"
-        # model_provider = "nvidia"
+        # # model_name = "deepseek-ai/deepseek-v3.1"
+        # # model_name = "deepseek-ai/deepseek-v3.1-terminus"
+        # # model_name = "minimaxai/minimax-m2"
+        # # model_name = "mistralai/mistral-nemotron"
+        # # model_name = "qwen/qwen3-next-80b-a3b-instruct"
+        # model_name = "qwen/qwen3-next-80b-a3b-thinking"
+        # # model_name = "moonshotai/kimi-k2-instruct-0905"
+        # model_name = "nvidia/llama-3.3-nemotron-super-49b-v1.5"
+        # # model_provider = "nvidia"
 
 
-        model = ChatOpenAI(
-            model=model_name,
-            openai_api_key=os.getenv("NVIDIA_API_KEY"),
-            openai_api_base="https://integrate.api.nvidia.com/v1"
-        )
+        # model = ChatOpenAI(
+        #     model=model_name,
+        #     openai_api_key=os.getenv("NVIDIA_API_KEY"),
+        #     openai_api_base="https://integrate.api.nvidia.com/v1"
+        # )
 
         agent = create_agent(model, tools)