Update app.py

app.py

@@ -1,67 +1,28 @@
+
+from __future__ import unicode_literals
+import subprocess
 import os
+#from dotenv import load_dotenv
+
+#load_dotenv()
+
+os.environ['USER_AGENT'] = 'myagent'
+
 import gradio as gr
 import requests
 import inspect
+import tempfile
+import nest_asyncio
 import pandas as pd
-from langchain_core.messages import HumanMessage
-from agent import build_graph
-
-# (Keep Constants as is)
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        self.graph = build_graph()
-
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Wrap the question in a HumanMessage from langchain_core
-        messages = [HumanMessage(content=question)]
-        messages = self.graph.invoke({"messages": messages})
-        answer = messages['messages'][-1].content
-        return answer[14:]
-    # 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
-
-def run_and_submit_all( profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
 
-    if profile:
-        username= f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
+from agent import MyAgent
+from tools import initialize_web_tools
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+nest_asyncio.apply()
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
-    try:
-        agent = BasicAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-    # 2. Fetch Questions
+def fetch_questions(questions_url):
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -71,6 +32,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
              print("Fetched questions list is empty.")
              return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
+        return questions_data
     except requests.exceptions.RequestException as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
@@ -82,29 +44,83 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         print(f"An unexpected error occurred fetching questions: {e}")
         return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
+def download_file(files_url, task_id):
+    try:
+        filename = task_id
+        response = requests.get(files_url+'/'+filename)
+        response.raise_for_status()
+
+        filename = response.headers['content-disposition'].split('"')[1]
+        temp_dir = tempfile.gettempdir()
+        file_path = os.path.join(temp_dir, filename)
+        with open(file_path, 'wb') as f:
+            f.write(response.content)
+
+        return file_path
+    except Exception as file_e:
+        print(f"No file found for task {task_id} or error: {file_e}")
+        return None
+
+async def run_my_agent(agent, questions_data, files_url):
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
         task_id = item.get("task_id")
+        file_path = download_file(files_url, task_id)
         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
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = await agent(question_text, file_path, task_id)
             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:
              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}"})
+    return results_log, answers_payload
+
+async def run_and_submit_all( profile: gr.OAuthProfile | None):
+    """
+    Fetches all questions, runs the BasicAgent on them, submits all answers,
+    and displays the results.
+    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
+    if profile:
+        username= f"{profile.username}"
+        print(f"User logged in: {username}")
+    else:
+        print("User not logged in.")
+        return "Please Login to Hugging Face with the button.", None
 
+    api_url = DEFAULT_API_URL
+    questions_url = f"{api_url}/questions"
+    submit_url = f"{api_url}/submit"
+    files_url = f"{api_url}/files"
+
+    # 1. Instantiate Agent ( modify this part to create your agent)
+    try:
+        web_tools = initialize_web_tools()
+        agent = MyAgent(web_tools=web_tools)
+    except Exception as e:
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
+    agent_code = f"https://huggingface.co/spaces/{space_id_startup}/tree/main"
+    print(agent_code)
+
+    # 2. Fetch Questions
+    questions_data = fetch_questions(questions_url)
+
+    # 3. Run your Agent
+    results_log, answers_payload = await run_my_agent(agent, questions_data, files_url)
+    
     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)
 
-    # 4. Prepare Submission 
+    # 4. Prepare Submission
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
@@ -179,7 +195,7 @@ with gr.Blocks() as demo:
 
     run_button.click(
         fn=run_and_submit_all,
-        outputs=[status_output, results_table]
+        outputs=[status_output, results_table],
     )
 
 if __name__ == "__main__":
@@ -204,4 +220,4 @@ 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)