Update app.py

app.py

@@ -1,25 +1,21 @@
-import os
-import gradio as gr
-import requests
-import pandas as pd
-import re
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- LangChain Agent Definition ---
 from langchain_huggingface import HuggingFaceEndpoint
 from langchain.agents import initialize_agent, Tool
 from langchain.agents.agent_types import AgentType
 from langchain.tools import tool
 from duckduckgo_search import DDGS
+import re
 
 @tool
 def search_web(query: str) -> str:
     """Search the web and return a relevant text snippet."""
-    with DDGS() as ddgs:
-        results = list(ddgs.text(query, max_results=3))
-    return results[0]["body"] if results else "No result"
+    try:
+        with DDGS() as ddgs:
+            results = list(ddgs.text(query, max_results=3))
+        if not results or not results[0].get("body"):
+            return "No result found"
+        return results[0]["body"]
+    except Exception as e:
+        return f"Web search failed: {e}"
 
 @tool
 def run_code_snippet(code: str) -> str:
@@ -30,15 +26,15 @@ def run_code_snippet(code: str) -> str:
         result = [v for v in local_env.values() if isinstance(v, (int, float, str))]
         return str(result[0]) if result else "0"
     except Exception as e:
-        return f"Error: {str(e)}"
+        return f"Error in code: {str(e)}"
 
 class GAIAAgent:
     def __init__(self):
         print("Initializing LangChain agent with Hugging FaceEndpoint...")
         self.llm = HuggingFaceEndpoint(
-            repo_id="google/flan-t5-xl",        # You can replace this with another model
+            repo_id="google/flan-t5-xl",
             temperature=0.3,
-            # model_kwargs={"max_length": 256}    # ✅ Fix: pass max_length via model_kwargs
+            model_kwargs={"max_length": 256}  # ✅ fix: max_length passed safely
         )
         self.tools = [search_web, run_code_snippet]
         self.agent = initialize_agent(
@@ -50,127 +46,22 @@ class GAIAAgent:
 
     def __call__(self, question: str) -> str:
         try:
+            print(f"[Agent] Invoking on question: {question}")
             result = self.agent.invoke(question)
+            print(f"[Agent] Raw result: {result}")
+
+            if not result or not isinstance(result, str):
+                raise StopIteration("Model returned empty or non-string result.")
+
             return f"FINAL ANSWER: {self.clean_answer(result)}"
+
+        except StopIteration as stop_err:
+            print(f"[Agent] StopIteration: {stop_err}")
+            return "FINAL ANSWER: Error: model or tool returned no result"
         except Exception as e:
+            print(f"[Agent] Exception: {e}")
             return f"FINAL ANSWER: Error: {e}"
 
     def clean_answer(self, ans: str) -> str:
         ans = re.sub(r"[\$,%]", "", ans)
         return ans.strip()
-
-# --- Evaluation & Submission Pipeline ---
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    space_id = os.getenv("SPACE_ID")
-
-    if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        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"
-
-    try:
-        agent = GAIAAgent()
-    except Exception as e:
-        return f"Error initializing agent: {e}", None
-
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            return "Fetched questions list is empty or invalid format.", None
-        print(f"Fetched {len(questions_data)} questions.")
-    except Exception as e:
-        return f"Error fetching questions: {e}", None
-
-    results_log = []
-    answers_payload = []
-    for item in questions_data:
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        if not task_id or question_text is None:
-            continue
-        try:
-            submitted_answer = 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:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
-
-    if not answers_payload:
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    try:
-        response = requests.post(submit_url, json=submission_data, timeout=60)
-        response.raise_for_status()
-        result_data = response.json()
-        final_status = (
-            f"Submission Successful!\n"
-            f"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
-            f"Message: {result_data.get('message', 'No message received.')}"
-        )
-        return final_status, pd.DataFrame(results_log)
-    except Exception as e:
-        return f"Submission Failed: {e}", pd.DataFrame(results_log)
-
-# --- Gradio UI ---
-with gr.Blocks() as demo:
-    gr.Markdown("# GAIA Agent Evaluation Runner")
-
-    gr.Markdown("""
-    **Instructions:**
-    1. Set your Hugging Face token in the Space Settings → Secrets as `HUGGINGFACEHUB_API_TOKEN`.
-    2. Log in to Hugging Face below to associate your username.
-    3. Run the agent to fetch, answer, and submit GAIA benchmark questions.
-    """)
-
-    gr.LoginButton()
-
-    gr.Markdown("## 🔍 Try a Question Preview")
-    preview_input = gr.Textbox(label="Your GAIA-style question")
-    preview_output = gr.Textbox(label="Agent's Response", lines=2)
-    preview_button = gr.Button("Preview Answer")
-    preview_button.click(lambda q: GAIAAgent()(q), inputs=preview_input, outputs=preview_output)
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-# --- Main Entry ---
-if __name__ == "__main__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST not set.")
-
-    if space_id_startup:
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f"   Repo: https://huggingface.co/spaces/{space_id_startup}")
-        print(f"   Code: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️  SPACE_ID not set.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-    print("Launching Gradio Interface for GAIA Agent Evaluation...")
-    demo.launch(debug=True, share=False)