Update app.py

app.py

@@ -1,57 +1,124 @@
-# This file fixes the ImportError caused by deprecated path for create_react_agent.
-# In modern LangChain (v0.1.0+), the import path has been simplified.
-
-# 1. CORRECTED IMPORT: create_react_agent is now imported directly from langchain.agents
-from langchain.agents import create_react_agent, AgentExecutor
-from langchain_core.prompts import ChatPromptTemplate
-from langchain_core.tools import Tool
-# Note: You would also need to import your LLM, e.g.,
-# from langchain_google_genai import ChatGoogleGenerativeAI
-# from langchain_community.llms import GooglePalm
-
-# --- Mock Setup (Replace with your actual agent logic) ---
-# Define a simple mock tool
-def get_current_time(query: str) -> str:
-    """Returns the current time based on the query."""
-    return "2025-11-05 16:54:00 (Mocked)"
-
-tools = [
-    Tool(
-        name="time_checker",
-        func=get_current_time,
-        description="A tool to check the current time for planning and scheduling."
-    )
-]
-
-# Define the prompt (often the ReAct agent uses a specific template)
-# This is a minimal example; you would use a more robust prompt in production.
-prompt = ChatPromptTemplate.from_messages(
-    [
-        ("system", "You are a helpful assistant. Use the tools provided to answer the user's query."),
-        ("human", "{input}"),
-        ("placeholder", "{agent_scratchpad}"),
-    ]
-)
+import os
+import requests
+import gradio as gr
 
-# Initialize a placeholder LLM for demonstration purposes
-class PlaceholderLLM:
-    def invoke(self, messages):
-        print("\n--- LLM INVOKED (Placeholder) ---")
-        return "Thought: I should use the time_checker tool.\nAction: time_checker\nAction Input: current time"
+# --- CORRECTED LANGCHAIN IMPORTS ---
+# create_react_agent and AgentExecutor are now imported directly from langchain.agents
+from langchain.agents import AgentExecutor, create_react_agent
+from langchain.tools import Tool
+# We need to import the specific ReAct prompt template from the hub
+from langchain import hub 
+from langchain_google_genai import ChatGoogleGenerativeAI
+from typing import List, Dict # Added for Gradio ChatInterface typing
 
-llm = PlaceholderLLM()
+# --- Configuration and Environment Setup ---
 
-# Create the agent
-# Note: This will now succeed because of the corrected import path.
+# IMPORTANT: Do NOT hardcode your API key.
+# Define GEMINI_API_KEY as a Space Secret in your Hugging Face Space Settings.
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+
+if not GEMINI_API_KEY:
+    # This message helps the user set up their Hugging Face Space correctly
+    print("WARNING: GEMINI_API_KEY environment variable not set. Running without API key.")
+    # We will raise a value error later during LLM initialization if it's actually needed.
+
+# Initialize Gemini LLM
 try:
-    agent = create_react_agent(llm, tools, prompt)
-    print("Successfully imported and created the ReAct agent starter.")
+    llm = ChatGoogleGenerativeAI(
+        model="gemini-2.5-flash",
+        temperature=0.3,
+        google_api_key=GEMINI_API_KEY
+    )
 except Exception as e:
-    print(f"An error occurred during agent creation (after import fix): {e}")
+    # Graceful exit if API key is missing during initialization
+    raise RuntimeError(
+        "Could not initialize ChatGoogleGenerativeAI. "
+        "Ensure GEMINI_API_KEY is set as a Space Secret."
+    ) from e
+
+# --- Tool Definition ---
+
+def get_crypto_price(symbol: str) -> str:
+    """Fetches the current price of a cryptocurrency symbol."""
+    try:
+        # Use lowercase symbol for CoinGecko API lookup
+        lookup_symbol = symbol.lower()
+        url = f"https://api.coingecko.com/api/v3/simple/price?ids={lookup_symbol}&vs_currencies=usd"
+        res = requests.get(url).json()
+        
+        # Check if the symbol exists in the response
+        price = res.get(lookup_symbol, {}).get('usd')
+        
+        if price:
+            return f"💰 The current price of {symbol.capitalize()} is ${price:,.2f}"
+        else:
+            # Check for common partial matches as suggestions
+            if not res and len(lookup_symbol) > 3:
+                 return f"❌ Could not find price for symbol '{symbol}'. Try the full name (e.g., 'bitcoin', 'ethereum')."
+            return f"❌ Could not find price for symbol '{symbol}'."
+            
+    except Exception as e:
+        return f"⚠️ Error fetching price: {str(e)}"
+
+# Create a Tool for the Agent
+crypto_tool = Tool(
+    name="Crypto Price Checker",
+    func=get_crypto_price,
+    description="Fetches the current cryptocurrency price using CoinGecko. Use this tool ONLY when the user asks for a specific crypto price (e.g., 'bitcoin', 'ethereum')."
+)
+
+# List of tools
+tools = [crypto_tool]
 
-# The AgentExecutor is what runs the agent logic
-# agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
+# --- Agent Initialization (CORRECTED) ---
+
+# 1. CORRECTED PROMPT: Pull the standard ReAct prompt from LangChain Hub.
+# This prompt is guaranteed to have the required '{tools}' and '{tool_names}' variables.
+prompt = hub.pull("hwchase17/react")
+
+# 2. Create the Agent
+# This uses the corrected 'create_react_agent' import
+agent = create_react_agent(llm, tools, prompt)
+
+# 3. Create the Agent Executor
+# This uses the corrected 'AgentExecutor' import
+agent_executor = AgentExecutor(
+    agent=agent, 
+    tools=tools, 
+    verbose=True,
+    handle_parsing_errors=True # Good practice for ReAct agents
+)
+
+# --- Gradio Interface Function ---
+
+# Added 'history' for ChatInterface compatibility
+def run_agent(user_query: str, history: List[List[str]]) -> str:
+    """The main function called by Gradio to run the agent."""
+    if not user_query:
+        return "Please enter a question for the agent."
+
+    try:
+        # Use the AgentExecutor's invoke method
+        # The input must be a dictionary
+        response: Dict = agent_executor.invoke({"input": user_query})
+        
+        # Extract the final answer text
+        return response.get("output", "Agent could not find a clear answer.")
+        
+    except Exception as e:
+        # Catch and report any runtime errors gracefully
+        return f"An error occurred while running the agent: {str(e)}"
+
+# --- Gradio App Setup (Using ChatInterface for better UX) ---
+
+gr.ChatInterface(
+    fn=run_agent,
+    title="💰 Gemini Crypto Price Agent (ReAct Pattern)",
+    description="Ask the agent to check the current price of cryptocurrencies (e.g., bitcoin, ethereum). This agent uses the modern LangChain ReAct pattern (v0.1.0+).",
+    examples=[
+        ["What is the current price of Bitcoin?"],
+        ["How much is Ethereum?"],
+        ["What about solana?"]
+    ]
+).launch()
 
-# Example usage (uncomment and replace PlaceholderLLM with a real LLM for execution)
-# response = agent_executor.invoke({"input": "What time is it right now?"})
-# print(f"\nFinal Response: {response['output']}")