Spaces:
Sleeping
Sleeping
| import os | |
| import gradio as gr | |
| from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI | |
| from langchain_community.agent_toolkits.load_tools import load_tools | |
| from langchain.agents import create_react_agent | |
| from langchain.prompts import PromptTemplate | |
| # Set up Google API keys from environment variables | |
| GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY') | |
| SERPER_API_KEY = os.getenv('SERPER_API_KEY') | |
| # Check if the keys are loaded correctly | |
| if GOOGLE_API_KEY is None or SERPER_API_KEY is None: | |
| raise ValueError("Please set the GOOGLE_API_KEY and SERPER_API_KEY environment variables.") | |
| # Initialize embeddings and language model | |
| gemini_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001") | |
| llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro") | |
| # Load tools | |
| tools = load_tools(["serpapi", "llm-math"], llm=llm, serpapi_api_key=SERPER_API_KEY) | |
| # Debug: Print tools to verify their structure | |
| print("Loaded tools:", tools) | |
| # Check if tools are loaded correctly | |
| if not all(hasattr(tool, 'name') and hasattr(tool, 'description') for tool in tools): | |
| raise ValueError("Loaded tools are not in the expected format.") | |
| # Define a prompt using PromptTemplate with required variables | |
| prompt = PromptTemplate( | |
| input_variables=["query", "agent_scratchpad", "tools", "tool_names"], | |
| template=( | |
| "You are a helpful assistant that answers questions based on the provided tools.\n" | |
| "Tools available: {tool_names}\n" | |
| "Current tools: {tools}\n" | |
| "Scratchpad: {agent_scratchpad}\n" | |
| "Question: {query}" | |
| ) | |
| ) | |
| # Initialize the agent with the prompt | |
| agent = create_react_agent(tools=tools, llm=llm, prompt=prompt) | |
| # Custom output parsing function | |
| def custom_output_parser(text): | |
| if "tool_code" in text: | |
| tool_code_start = text.find("```tool_code") + len("```tool_code") | |
| tool_code_end = text.find("```", tool_code_start) | |
| tool_code = text[tool_code_start:tool_code_end].strip() | |
| return {"tool_code": tool_code} | |
| return {"text": text} | |
| # Function to run the agent | |
| def search(query): | |
| inputs = { | |
| "query": query, | |
| "agent_scratchpad": "", # Initial empty scratchpad | |
| "tools": tools, | |
| "tool_names": ", ".join([tool.name for tool in tools]), | |
| "intermediate_steps": [] # Initial empty intermediate steps | |
| } | |
| try: | |
| output = agent.invoke(inputs) | |
| # Process the output with the custom output parser | |
| parsed_output = custom_output_parser(output) | |
| # Execute tool code if exists | |
| if "tool_code" in parsed_output: | |
| tool_code = parsed_output["tool_code"] | |
| exec_globals = {"search": tools[0].func} # Assuming 'search' is the first tool | |
| exec(tool_code, exec_globals) | |
| return exec_globals.get("result", "Executed tool code.") | |
| return parsed_output["text"] | |
| except Exception as e: | |
| # Print the exception and the inputs for debugging | |
| print(f"Error: {e}") | |
| print("Inputs:", inputs) | |
| return str(e) | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=search, | |
| inputs=gr.Textbox(label="Enter your search query", placeholder="What is the hometown of the reigning men's U.S. Open champion?"), | |
| outputs="text", | |
| title="Custom Search Engine", | |
| description="A search engine powered by LangChain and Google Generative AI. Enter your query to get started!", | |
| theme="default" | |
| ) | |
| # Launch the interface with share=True for a public link | |
| iface.launch(share=True) | |