Update app.py

app.py

@@ -1,215 +1,50 @@
-import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-
-# Try to import Google GenAI SDK
-try:
-    from google import genai
-except ImportError:
-    raise ImportError("Please install the 'google-genai' package: pip install google-genai")
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-# --- Gemini Agent Definition ---
-class GeminiAgent:
+# Add this class replacing the current GeminiAgent
+class MultiAgent:
     def __init__(self):
-        print("GeminiAgent initialized.")
-        api_key = os.getenv("GEMINI_API_KEY")
-        if not api_key:
-            raise ValueError("GEMINI_API_KEY environment variable not set.")
-        # Instantiate the Gemini API client
-        self.client = genai.Client(api_key=api_key)
+        print("MultiAgent initialized with Gemini fallback.")
 
-    def __call__(self, question: str) -> str:
-        print(f"GeminiAgent received question (first 50 chars): {question[:50]}...")
-        # Prompt the model to answer concisely without explanation
-        prompt = f"Answer concisely without explanation: {question}"
-        response = self.client.models.generate_content(
-            model="gemini-2.0-flash", contents=prompt
-        )
-        answer = response.text.strip()
-        # Remove trailing punctuation if any
-        answer = answer.rstrip(".!?")
-        print(f"GeminiAgent returning answer: {answer}")
-        return answer
+        # Load primary Gemini key
+        self.gemini_key = os.getenv("GEMINI_API_KEY")
+        self.alt_key = os.getenv("GEMINI_API_KEY_2")  # Second key (optional)
 
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the GeminiAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID")  # Get the SPACE_ID for sending link to the code
+        if not self.gemini_key:
+            raise ValueError("GEMINI_API_KEY environment variable not set.")
 
-    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
+        import google.generativeai as genai
+        self.genai = genai
 
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
+        # Primary client
+        self.client_primary = genai.Client(api_key=self.gemini_key)
+        # Secondary client (fallback)
+        self.client_secondary = genai.Client(api_key=self.alt_key) if self.alt_key else None
 
-    # 1. Instantiate Agent
-    try:
-        agent = GeminiAgent()
-    except Exception as e:
-        print(f"Error instantiating agent: {e}")
-        return f"Error initializing agent: {e}", None
-    # In the case of an app running as a Hugging Face space, this link points
-    # toward your codebase (useful for others so please keep it public)
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+        self.model_name = "gemini-2.5-flash-preview-04-17"
 
-    # 2. Fetch Questions
-    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}")
-        print(f"Response text: {response.text[:500]}")
-        return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
+    def _query_model(self, client, question):
+        prompt = f"Answer concisely without explanation: {question}"
+        response = client.models.generate_content(
+            model=self.model_name,
+            contents=prompt
+        )
+        return response.text.strip().rstrip(".!?")
 
-    # 3. Run your Agent
-    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 question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
+    def __call__(self, question: str) -> str:
+        print(f"MultiAgent received question: {question[:50]}...")
         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})
+            return self._query_model(self.client_primary, question)
         except Exception as 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:
-        print("Agent did not produce any answers to submit.")
-        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
-
-    # 4. Prepare Submission
-    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. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    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.')}"
-        )
-        print("Submission successful.")
-        results_df = pd.DataFrame(results_log)
-        return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Gemini 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 separate action or even to answer the questions asynchronously.
-        """
-    )
-    gr.LoginButton()
-    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__":
-    print("\n" + "-"*30 + " App Starting " + "-"*30)
-    # Check for SPACE_HOST and SPACE_ID at startup for information
-    space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f" Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("��️ SPACE_HOST environment variable not found (running locally?).")
-
-    if space_id_startup:  # Print repo URLs if SPACE_ID is found
-        print(f"✅ SPACE_ID found: {space_id_startup}")
-        print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
-        print(f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
-    else:
-        print("ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Gemini Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+            print(f"⚠️ Primary Gemini failed: {e}")
+            if self.client_secondary:
+                try:
+                    print("Attempting fallback Gemini key...")
+                    return self._query_model(self.client_secondary, question)
+                except Exception as e2:
+                    print(f"❌ Secondary Gemini failed: {e2}")
+                    return "AGENT ERROR"
+            else:
+                return "AGENT ERROR"
+
+# Then replace:
+# agent = GeminiAgent()
+# with:
+# agent = MultiAgent()