update app.py

app.py

@@ -1,25 +1,35 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from langgraph.agent import langgraph_agent
+from llamaindex.agent import llamaindex_agent
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
+FRAMEWORK = "LANGGRAPH"
+# FRAMEWORK = "LLAMAINDEX"
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
+        if FRAMEWORK == "LANGGRAPH":
+            self.agent = langgraph_agent()
+        else:
+            self.agent = llamaindex_agent()
         print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
+        
+    async 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
+        if FRAMEWORK == "LANGGRAPH":
+            answer = self.agent(question)
+        else:
+            answer = await self.agent(question)
+
+        return answer
 
-def run_and_submit_all( profile: gr.OAuthProfile | None):
+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.
@@ -73,14 +83,26 @@ 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:
         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
         try:
-            submitted_answer = agent(question_text)
+            if file_name:
+                # add the URL of the data source to the question (so that the agent can deal with it)
+                file_url = f"{DEFAULT_API_URL}/files/{task_id}"
+                question_text += f'\nFile URL: "{file_url}"'
+                # get the extension of the file to help the agent
+                try:
+                    ext = file_name.split('.')[-1]
+                    question_text += f" (.{ext} file)"
+                except:
+                    pass
+            submitted_answer = await agent(question_text)
             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:
@@ -139,7 +161,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
+# run_and_submit_all(None)
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
     gr.Markdown("# Basic Agent Evaluation Runner")