debugging

app.py

@@ -3,28 +3,29 @@ import gradio as gr
 import requests
 import pandas as pd
 import asyncio
-from langgraph_agents import graph  # Use your agent
 from typing import Optional
 
+from langchain_core.messages import HumanMessage
+from langgraph_agents import graph  # Your graph agent
+
 # Constants
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 user_answers_cache = {}  # session-based cache
 
+
 class GaiaAgent:
     def __init__(self):
         print("Graph-based agent initialized.")
 
     def __call__(self, question: str) -> str:
         print("Received question:", question)
-        state = {"question": question, "answer": ""}
         try:
-            result = graph.invoke(state)
-            print("Result type:", type(result))
-            print("Result value:", result)
-            if isinstance(result, dict):
-                return result.get("answer", "No answer generated.")
-            else:
-                return f"Unexpected output from graph: {result}"
+            # FIXED: Correct input for LangGraph
+            result = graph.invoke({"messages": [HumanMessage(content=question)]})
+            messages = result.get("messages", [])
+            if messages:
+                return messages[-1].content.strip()
+            return "No messages returned."
         except Exception as e:
             return f"ERROR invoking graph: {e}"
 
@@ -62,6 +63,7 @@ async def run_agent(profile: gr.OAuthProfile | None):
     return f"Answered {len(results)} questions. Ready to submit.", df
 
 
+# Submission
 def submit_answers(profile: gr.OAuthProfile | None):
     if not profile:
         return "Please login to Hugging Face.", None
@@ -77,9 +79,12 @@ def submit_answers(profile: gr.OAuthProfile | None):
 
     space_id = os.getenv("SPACE_ID", "")
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""
-    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+    submission_data = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload,
+    }
 
-    # 3. Submit to scoring API
     try:
         response = requests.post(f"{DEFAULT_API_URL}/submit", json=submission_data, timeout=60)
         response.raise_for_status()

langgraph_agents.py

@@ -132,21 +132,24 @@ qa_pairs = [
     }
 ]
 
-# Run evaluation
-print("\n📊 Evaluating QA Agent\n")
-
-for idx, qa in enumerate(qa_pairs, 1):
-    question = qa["q"]
-    ground_truth = qa["gt"]
-
-    print(f"🔹 Q{idx}: {question}")
-
-    try:
-        result = graph.invoke({"messages": [HumanMessage(content=question)]})
-        raw_answer = result["messages"][-1].content.strip()
-        reflected = reflect_answer(question, raw_answer)
-        score = similarity_score(reflected, ground_truth)
-        verdict = "✅" if score == 1.0 else "❌"
-        print(f"{verdict} A{idx}: {reflected}  |  GT: {ground_truth}  |  Similarity: {score}\n")
-    except Exception as e:
-        print(f"❌ A{idx} ERROR: {e}\n")
+
+if __name__ == "__main__":
+    # Run only if script is executed directly, NOT imported
+
+    print("\n📊 Evaluating QA Agent\n")
+
+    for idx, qa in enumerate(qa_pairs, 1):
+        question = qa["q"]
+        ground_truth = qa["gt"]
+
+        print(f"🔹 Q{idx}: {question}")
+
+        try:
+            result = graph.invoke({"messages": [HumanMessage(content=question)]})
+            raw_answer = result["messages"][-1].content.strip()
+            reflected = reflect_answer(question, raw_answer)
+            score = similarity_score(reflected, ground_truth)
+            verdict = "✅" if score == 1.0 else "❌"
+            print(f"{verdict} A{idx}: {reflected}  |  GT: {ground_truth}  |  Similarity: {score}\n")
+        except Exception as e:
+            print(f"❌ A{idx} ERROR: {e}\n")