Agent llm changed to duckduckgo

app.py

@@ -1,40 +1,43 @@
 import os
-import gradio as gr
 import requests
-import pandas as pd
-
-from smolagents import Tool
-from duckduckgo_search import DDGS
+import gradio as gr
+from huggingface_hub import login
 from smolagents import CodeAgent
+from smolagents.models import HfApiModel
+from duckduckgo_search import ddg_search  # Importiert DuckDuckGo Search
 
-class DuckDuckGoTool(Tool):
-    name = "duckduckgo"
-    description = "Use this tool to search the web using DuckDuckGo."
-    inputs = {"query": "text"}  # <<< dict erwartet!
-    output_type = "text"
-
-    def __call__(self, inputs: dict) -> str:
-        query = inputs.get("query", "")
-        if not query:
-            return "No query provided."
-        
+# Tool: DuckDuckGoSearchTool
+class DuckDuckGoTool:
+    def __init__(self):
+        self.name = "DuckDuckGoSearch"
+        self.description = "Tool to search DuckDuckGo for an answer."
+
+    def __call__(self, query: str) -> str:
+        """Durchführt eine Suche mit DuckDuckGo und gibt die ersten 3 Ergebnisse zurück."""
         try:
-            with DDGS() as ddgs:
-                results = ddgs.text(query, max_results=3)
-                if not results:
-                    return "No results found."
-                return "\n\n".join(r.get("body", "") for r in results if r.get("body"))
+            search_results = ddg_search(query)
+            results = [result["title"] + " - " + result["url"] for result in search_results[:3]]
+            return "\n".join(results)
         except Exception as e:
-            return f"Search failed: {e}"
+            return f"Fehler bei der DuckDuckGo-Suche: {e}"
 
-# --- Agent Definition ---
+# Agent: MySmolAgent
 class MySmolAgent:
     def __init__(self):
         print("Initializing SmolAgent...")
-        login(token=os.getenv("HF_TOKEN"))  # sicheres Laden über Umgebungsvariable
 
+        # Sicherstellen, dass der Token korrekt gesetzt ist
+        token = os.getenv("HF_TOKEN")
+        if not token:
+            raise ValueError("HF_TOKEN environment variable is missing.")
+
+        # Anmeldung bei HuggingFace
+        login(token=token)
+
+        # Modell initialisieren
         self.model = HfApiModel(model="mistralai/Mistral-7B-Instruct-v0.1")
 
+        # Agent mit Tools und Modell einrichten
         self.agent = CodeAgent(
             tools=[DuckDuckGoTool()],
             model=self.model,
@@ -47,74 +50,138 @@ class MySmolAgent:
         print(f"Agent running for question: {question[:60]}...")
         return self.agent.run(question)
 
-# --- Evaluation & Submission Handler ---
+# Funktion, um Fragen zu beantworten und die Antworten zu senden
 def run_and_submit_all(profile: gr.OAuthProfile | None):
+    """
+    Holt alle Fragen, lässt den Agenten sie beantworten, sendet alle Antworten,
+    und zeigt die Ergebnisse an.
+    """
+    # --- Bestimmen der Hugging Face API-URLs ---
+    space_id = os.getenv("SPACE_ID")
+
+    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 = "https://agents-course-unit4-scoring.hf.space"
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
-    username = profile.username if profile else None
-
-    if not username:
-        return "Please login with Hugging Face", None
 
+    # 1. Instanziiere den Agenten
     try:
         agent = MySmolAgent()
     except Exception as e:
-        return f"Fehler beim Initialisieren des Agents: {e}", None
+        print(f"Error instantiating agent: {e}")
+        return f"Error initializing agent: {e}", None
 
+    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
+    print(agent_code)
+
+    # 2. Frage abrufen
+    print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
         response.raise_for_status()
         questions_data = response.json()
+        if not questions_data:
+            print("Fetched questions list is empty.")
+            return "Fetched questions list is empty or invalid format.", None
+        print(f"Fetched {len(questions_data)} questions.")
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        return f"Error fetching questions: {e}", None
+    except requests.exceptions.JSONDecodeError as e:
+         print(f"Error decoding JSON response from questions endpoint: {e}")
+         return f"Error decoding server response for questions: {e}", None
     except Exception as e:
-        return f"Fehler beim Abrufen der Fragen: {e}", None
+        print(f"An unexpected error occurred fetching questions: {e}")
+        return f"An unexpected error occurred fetching questions: {e}", None
 
+    # 3. Agent ausführen
     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")
-        if not task_id or not question_text:
+        if not task_id or question_text is None:
+            print(f"Skipping item with missing task_id or question: {item}")
             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})
+            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"ERROR: {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}"})
 
     if not answers_payload:
-        return "Keine Antworten erzeugt.", pd.DataFrame(results_log)
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    submission = {
-        "username": username,
-        "agent_code": "https://huggingface.co/spaces/YOUR_SPACE_ID/tree/main",
-        "answers": answers_payload
-    }
+    # 4. Bereite Submission vor
+    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)
 
+    # 5. Einreichung
+    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
     try:
-        r = requests.post(submit_url, json=submission, timeout=60)
-        r.raise_for_status()
-        data = r.json()
-        msg = (
-            f"Submission erfolgreich!\n"
-            f"User: {data.get('username')}\n"
-            f"Score: {data.get('score')}% "
-            f"({data.get('correct_count')}/{data.get('total_attempted')} korrekt)"
+        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 msg, pd.DataFrame(results_log)
+        print("Submission successful.")
+        results_df = pd.DataFrame(results_log)
+        return final_status, results_df
+    except requests.exceptions.RequestException as e:
+        error_detail = f"Submission Failed: Network error - {e}"
+        print(error_detail)
+        results_df = pd.DataFrame(results_log)
+        return error_detail, results_df
     except Exception as e:
-        return f"Fehler bei der Übermittlung: {e}", pd.DataFrame(results_log)
+        status_message = f"An unexpected error occurred during submission: {e}"
+        print(status_message)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
 
-# --- Gradio UI ---
+# Gradio-Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# DuckDuckGo-Agent (kostenlos)")
+    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown(
+        """
+        **Instructions:**
+        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
+        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        ---
+        **Disclaimers:**
+        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
+        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
+        """
+    )
+
     gr.LoginButton()
-    run_button = gr.Button("Fragen beantworten und abschicken")
-    status = gr.Textbox(label="Status", lines=5)
-    results = gr.DataFrame(label="Antworten")
 
-    run_button.click(fn=run_and_submit_all, outputs=[status, results])
+    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]
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
+    demo.launch(debug=True, share=False)