Update app.py

app.py

@@ -1,45 +1,108 @@
-from langchain_openai import ChatOpenAI
-from langchain_community.tools import DuckDuckGoSearchRun
+import os
+import gradio as gr
+import requests
+import pandas as pd
+import openai
 
-class BasicAgent:
+# Load OpenAI API Key from environment
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+
+# --- Smart Agent Definition ---
+class SmartAgent:
     def __init__(self):
         print("SmartAgent initialized.")
-        self.llm = ChatOpenAI(model="gpt-4", temperature=0)
-        self.search_tool = DuckDuckGoSearchRun()
-    
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question[:50]}...")
 
-        # Step 1: Ask LLM if it can confidently answer or needs to search
+    def __call__(self, question: str) -> str:
+        print(f"[Agent] Processing question: {question[:50]}...")
         prompt = f"""
-You are a helpful assistant. Given the question below, decide if you know the answer with high confidence.
-If yes, answer concisely.
-If you need more information, respond with SEARCH: followed by the search query.
-Question: {question}
-Answer or SEARCH:
-"""
-        response = self.llm.invoke(prompt).content.strip()
-
-        if response.upper().startswith("SEARCH:"):
-            # Extract search query
-            query = response[len("SEARCH:"):].strip()
-            print(f"Performing search with query: {query}")
-            
-            # Use DuckDuckGo search to get info
-            search_result = self.search_tool.run(query)
-            
-            # Step 2: Now use LLM again with search results to answer
-            prompt2 = f"""
-You are a helpful assistant. Use the following search results to answer the question concisely.
-Question: {question}
-Search Results: {search_result}
-Answer:
+You are an expert AI agent. Answer the following question clearly and precisely. 
+If external data is required (e.g. Wikipedia), use your knowledge base.
+Only return the final answer. Do NOT include 'Answer:' or any extra words.
+
+Question:
+{question}
 """
-            final_answer = self.llm.invoke(prompt2).content.strip()
-            print(f"Agent final answer: {final_answer}")
-            return final_answer
-        
-        else:
-            # LLM confident answer directly
-            print(f"Agent direct answer: {response}")
-            return response
+        try:
+            response = openai.ChatCompletion.create(
+                model="gpt-4",
+                messages=[{"role": "user", "content": prompt}],
+                temperature=0.4,
+                max_tokens=300
+            )
+            answer = response.choices[0].message["content"].strip()
+            print(f"[Agent] Answer: {answer}")
+            return answer
+        except Exception as e:
+            print(f"[Agent Error]: {e}")
+            return "Error: Could not generate answer."
+
+# --- Submission Logic ---
+def run_and_submit_all(profile: gr.OAuthProfile | None):
+    space_id = os.getenv("SPACE_ID", "your_username/your_space_name")
+
+    if profile:
+        username = profile.username
+        print(f"User logged in: {username}")
+    else:
+        return "❌ Please log in to Hugging Face first.", None
+
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    agent = SmartAgent()
+
+    try:
+        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
+        response.raise_for_status()
+        questions = response.json()
+    except Exception as e:
+        return f"❌ Failed to fetch questions: {e}", None
+
+    results_log = []
+    answers_payload = []
+
+    for item in questions:
+        task_id = item.get("task_id")
+        question_text = item.get("question")
+        if not task_id or not question_text:
+            continue
+        try:
+            answer = agent(question_text)
+            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": answer})
+        except Exception as e:
+            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"Error: {e}"})
+
+    submission = {
+        "username": username,
+        "agent_code": agent_code,
+        "answers": answers_payload
+    }
+
+    try:
+        resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
+        resp.raise_for_status()
+        result = resp.json()
+        score = result.get("score", "N/A")
+        correct = result.get("correct_count", "?")
+        total = result.get("total_attempted", "?")
+
+        msg = f"✅ Submission Successful!\nUser: {username}\nScore: {score}% ({correct}/{total} correct)"
+        return msg, 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 Submission Tool")
+    gr.Markdown("Click below to log in and run your agent on the GAIA benchmark.")
+    gr.LoginButton()
+    run_btn = gr.Button("Run Evaluation & Submit All Answers")
+    status = gr.Textbox(label="Status", lines=5)
+    table = gr.DataFrame(label="Results")
+
+    run_btn.click(fn=run_and_submit_all, outputs=[status, table])
+
+if __name__ == "__main__":
+    print("Launching GAIA Agent App")
+    demo.launch()