Spaces:
Sleeping
Sleeping
Updated
Browse files
app.py
CHANGED
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@@ -3,148 +3,32 @@ import gradio as gr
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import requests
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import inspect
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import pandas as pd
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from
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#
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def debug_environment():
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"""Print available environment variables related to API keys (with values hidden)"""
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debug_vars = [
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"HF_API_TOKEN", "HUGGINGFACEHUB_API_TOKEN",
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"OPENAI_API_KEY", "XAI_API_KEY",
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"AGENT_MODEL_TYPE", "AGENT_MODEL_ID",
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"AGENT_TEMPERATURE", "AGENT_VERBOSE"
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]
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print("=== DEBUG: Environment Variables ===")
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for var in debug_vars:
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if os.environ.get(var):
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# Hide actual values for security
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print(f"{var}: [SET]")
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else:
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print(f"{var}: [NOT SET]")
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print("===================================")
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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# --- Basic Agent Definition ---
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# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
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class BasicAgent:
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def __init__(self):
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print("
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#
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#
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import dotenv
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dotenv.load_dotenv()
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print("Loaded environment variables from .env file")
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except ImportError:
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print("python-dotenv not installed, continuing with environment as is")
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# Try to load API keys from environment
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# Check both HF_API_TOKEN and HUGGINGFACEHUB_API_TOKEN (HF Spaces uses HF_API_TOKEN)
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hf_token = os.environ.get("HF_API_TOKEN") or os.environ.get("HUGGINGFACEHUB_API_TOKEN")
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openai_key = os.environ.get("OPENAI_API_KEY")
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xai_key = os.environ.get("XAI_API_KEY")
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# If we have at least one API key, use a model-based approach
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if hf_token:
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# Default model parameters - read directly from environment
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model_type = os.environ.get("AGENT_MODEL_TYPE", "OpenAIServerModel")
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model_id = os.environ.get("AGENT_MODEL_ID", "gpt-4o")
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temperature = float(os.environ.get("AGENT_TEMPERATURE", "0.2"))
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verbose = os.environ.get("AGENT_VERBOSE", "false").lower() == "true"
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print(f"Agent config - Model Type: {model_type}, Model ID: {model_id}")
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try:
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if model_type == "HfApiModel" and hf_token:
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# Use Hugging Face API
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self.gaia_agent = GAIAAgent(
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model_type="HfApiModel",
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model_id=model_id,
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api_key=hf_token,
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temperature=temperature,
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executor_type="local",
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verbose=verbose
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)
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print(f"Using HfApiModel with model_id: {model_id}")
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else:
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# Fallback to using whatever token we have
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print("WARNING: Using fallback initialization with available token")
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if hf_token:
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self.gaia_agent = GAIAAgent(
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model_type="HfApiModel",
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# model_id="mistralai/Mistral-7B-Instruct-v0.2",
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model_id="mistralai/Mistral-7B-Instruct-v0.1",
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api_key=hf_token,
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temperature=temperature,
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executor_type="local",
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verbose=verbose
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)
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else:
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self.gaia_agent = GAIAAgent(
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model_type="OpenAIServerModel",
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model_id="grok-3-latest",
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api_key=xai_key,
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api_base=os.environ.get("XAI_API_BASE", "https://api.x.ai/v1"),
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temperature=temperature,
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executor_type="local",
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verbose=verbose
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)
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except ImportError as ie:
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# Handle OpenAI module errors specifically
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if "openai" in str(ie).lower() and hf_token:
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print(f"OpenAI module error: {ie}. Falling back to HfApiModel.")
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self.gaia_agent = GAIAAgent(
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model_type="HfApiModel",
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# model_id="mistralai/Mistral-7B-Instruct-v0.2",
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model_id="mistralai/Mistral-7B-Instruct-v0.1",
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api_key=hf_token,
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temperature=temperature,
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executor_type="local",
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verbose=verbose
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)
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print(f"Using HfApiModel with model_id: mistralai/Mistral-7B-Instruct-v0.2 (fallback)")
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else:
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raise
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else:
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# No API keys available, log the error
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print("ERROR: No API keys found. Please set at least one of these environment variables:")
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print("- HUGGINGFACEHUB_API_TOKEN or HF_API_TOKEN")
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print("- OPENAI_API_KEY")
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print("- XAI_API_KEY")
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self.gaia_agent = None
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print("WARNING: No API keys found. Agent will not be able to answer questions.")
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except Exception as e:
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print(f"Error initializing GAIAAgent: {e}")
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self.gaia_agent = None
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print("WARNING: Failed to initialize agent. Falling back to basic responses.")
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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try:
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# Process the question using the GAIA agent
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answer = self.gaia_agent.answer_question(question)
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print(f"Agent generated answer: {answer[:50]}..." if len(answer) > 50 else f"Agent generated answer: {answer}")
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return answer
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except Exception as e:
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print(f"Error processing question: {e}")
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# Fall back to a simple response on error
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return "An error occurred while processing your question. Please check the agent logs for details."
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else:
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# We don't have a valid agent, provide a basic response
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return "The agent is not properly initialized. Please check your API keys and configuration."
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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import requests
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import inspect
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import pandas as pd
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from gemini_agent import GeminiAgent
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# Constants
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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class BasicAgent:
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def __init__(self):
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print("Initializing the BasicAgent")
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# Load environment variables
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load_dotenv()
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# Get Gemini API key
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api_key = os.getenv('GOOGLE_API_KEY')
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if not api_key:
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raise ValueError("GOOGLE_API_KEY environment variable not set.")
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# Initialize GeminiAgent
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self.agent = GeminiAgent(api_key=api_key)
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print("GeminiAgent initialized successfully")
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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final_answer = self.agent.run(question)
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print(f"Agent returning fixed answer: {final_answer}")
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return final_answer
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def run_and_submit_all( profile: gr.OAuthProfile | None):
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"""
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