Update app.py

app.py

@@ -1,13 +1,11 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
-from agno.agent import Agent 
-from agno.tools.duckduckgo import DuckDuckGoTools
-from agno.models.nvidia import Nvidia
 from dotenv import load_dotenv
+from langchain_google_genai import ChatGoogleGenerativeAI
 
+# Load environment variables
 load_dotenv()
 
 # --- Constants ---
@@ -16,52 +14,55 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        self.agent=Agent(
-            model=Nvidia(id="meta/llama-3.3-70b-instruct"),
-            instructions='''
-            ## 🚀 Gaia Taskmaster: The Ultimate Agent Efficiency Prompt! 🌍
-
-                You are a high-performance AI agent with a laser focus on completing Gaia tasks with maximum efficiency and precision. Think of yourself as a blend of a master strategist and a productivity guru—always optimizing, always delivering.
-
-                ### Operational Guidelines for Every Gaia Task
-
-                - **Use the search tool and all available resources to gather the most current, accurate information.**
-                - **Present solutions with clarity, logical structure, and a results-driven mindset.**
-                - **Structure your responses in clear sections:**
-                - Task Overview
-                - Step-by-step Execution Plan
-                - Key Details, Data, or Code Snippets
-                - Impact Analysis or Next Steps
-                - **Keep responses concise but comprehensive (2-3 paragraphs or bullet points max).**
-                - **Apply best practices for UI stability and code formatting to ensure all outputs are organized, visible, and maintainable.**
-                - **End with a motivating sign-off or call to action, such as:**
-                - "Task completed—ready for the next challenge!"
-                - "Gaia task executed with precision. What’s next?"
-                - "Mission accomplished. Awaiting further instructions!"
-
-                _Remember: Always verify facts, optimize for efficiency, and maintain a focus on clear, actionable results!_
-                ''',
-            tools=[DuckDuckGoTools()])
-        print("BasicAgent initialized.") 
-        
+        self.llm = ChatGoogleGenerativeAI(
+            model="gemini-2.0-flash-001",
+            temperature=0.1,
+            max_tokens=1024,
+            api_key=os.getenv("GOOGLE_API_KEY"),
+            streaming=False  # Set to True if you want streaming output
+        )
+        self.instructions = '''
+## 🚀 Gaia Taskmaster: The Ultimate Agent Efficiency Prompt! 🌍
+
+You are a high-performance AI agent with a laser focus on completing Gaia tasks with maximum efficiency and precision. Think of yourself as a blend of a master strategist and a productivity guru—always optimizing, always delivering.
+
+### Operational Guidelines for Every Gaia Task
+
+- **Use the search tool and all available resources to gather the most current, accurate information.**
+- **Present solutions with clarity, logical structure, and a results-driven mindset.**
+- **Structure your responses in clear sections:**
+  - Task Overview
+  - Step-by-step Execution Plan
+  - Key Details, Data, or Code Snippets
+  - Impact Analysis or Next Steps
+- **Keep responses concise but comprehensive (2-3 paragraphs or bullet points max).**
+- **Apply best practices for UI stability and code formatting to ensure all outputs are organized, visible, and maintainable.**
+- **End with a motivating sign-off or call to action, such as:**
+  - "Task completed—ready for the next challenge!"
+  - "Gaia task executed with precision. What’s next?"
+  - "Mission accomplished. Awaiting further instructions!"
+
+_Remember: Always verify facts, optimize for efficiency, and maintain a focus on clear, actionable results!_
+        '''
+        print("BasicAgent (Gemini via LangChain) initialized.")
+
     def __call__(self, question: str) -> str:
+        prompt = f"{self.instructions}\n\n{question}"
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = self.agent.print_response(
-        question, stream=True
-        )
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        response = self.llm.invoke(prompt)
+        answer = response.content.strip() if hasattr(response, "content") else str(response)
+        print(f"Agent returning answer: {answer}")
+        return answer
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent 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
+    space_id = os.getenv("SPACE_ID")  # For codebase link
 
     if profile:
-        username= f"{profile.username}"
+        username = f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -71,13 +72,13 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent ( modify this part to create your agent)
+    # 1. Instantiate Agent
     try:
         agent = BasicAgent()
     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)
 
@@ -88,16 +89,16 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         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("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
+        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
@@ -117,8 +118,8 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             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:
-             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}"})
+            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.")
@@ -202,9 +203,8 @@ with gr.Blocks() as demo:
 
 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
+    space_id_startup = os.getenv("SPACE_ID")
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -212,7 +212,7 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup: # Print repo URLs if SPACE_ID is found
+    if space_id_startup:
         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")