Update app.py

app.py

@@ -4,7 +4,6 @@ import matplotlib.pyplot as plt
 import pandas as pd
 from langgraph.graph import StateGraph
 from langchain_core.messages import HumanMessage, AIMessage
-from langchain_anthropic import ChatAnthropic
 import warnings
 warnings.filterwarnings("ignore")
 
@@ -20,11 +19,51 @@ api_key = os.getenv("ANTHROPIC_API_KEY")
 if not api_key:
     raise ValueError("ANTHROPIC_API_KEY environment variable not set")
 
-# Fix: Initialize ChatAnthropic correctly without any proxy settings
-llm = ChatAnthropic(
-    anthropic_api_key=api_key,  # Changed from api_key to anthropic_api_key
-    model="claude-3-5-sonnet-20240229"
-)
+# Create a custom LLM implementation to avoid the proxies issue
+def create_llm():
+    # Directly use the Anthropic client instead of LangChain's wrapper
+    from anthropic import Anthropic
+    
+    # Create the base client without any proxies
+    client = Anthropic(api_key=api_key)
+    
+    # Create a simple wrapper function that mimics the LangChain interface
+    class CustomAnthropicLLM:
+        def __init__(self, client, model):
+            self.client = client
+            self.model = model
+        
+        def invoke(self, inputs):
+            if isinstance(inputs, dict) and "messages" in inputs:
+                messages = inputs["messages"]
+                formatted_messages = []
+                
+                for msg in messages:
+                    role = "user" if isinstance(msg, HumanMessage) else "assistant"
+                    formatted_messages.append({"role": role, "content": msg.content})
+                
+                response = self.client.messages.create(
+                    model=self.model,
+                    messages=formatted_messages,
+                    max_tokens=1024
+                )
+                return response.content[0].text
+            
+            elif isinstance(inputs, str):
+                response = self.client.messages.create(
+                    model=self.model, 
+                    messages=[{"role": "user", "content": inputs}],
+                    max_tokens=1024
+                )
+                return response.content[0].text
+            
+            else:
+                raise ValueError(f"Unsupported input format: {type(inputs)}")
+    
+    return CustomAnthropicLLM(client, "claude-3-5-sonnet-20240229")
+
+# Create our custom LLM
+llm = create_llm()
 
 # System prompt constructor
 def make_system_prompt(suffix: str) -> str: