Update app.py

app.py

@@ -3,21 +3,18 @@ import openai
 import gradio as gr
 import json
 import plotly.graph_objects as go
-from variables import CONTEXTUAL_ZOOM_PROMPT, CONTEXTUAL_ZOOM_default_response
+from variables import *
 
 class ArtExplorer:
   def __init__(self):
-      print("Initializing ArtExplorer...")
       self.client = openai.OpenAI(
           base_url="https://api.groq.com/openai/v1",
           api_key=os.environ.get("GROQ_API_KEY")
       )
-      print("OpenAI client initialized")
       self.current_state = {
           "zoom_level": 0,
           "selections": {}
       }
-      print("Current state initialized")
 
   def create_map(self, locations):
       """Create a Plotly map figure from location data"""
@@ -42,54 +39,41 @@ class ArtExplorer:
       )
       return fig
 
-def get_llm_response(self, query: str, zoom_context: dict = None) -> dict:
-  try:
-      current_zoom_states = {
-          "temporal": {"level": self.current_state["zoom_level"], "selection": ""},
-          "geographical": {"level": self.current_state["zoom_level"], "selection": ""},
-          "style": {"level": self.current_state["zoom_level"], "selection": ""},
-          "subject": {"level": self.current_state["zoom_level"], "selection": ""}
-      }
-
-      if zoom_context:
-          for key, value in zoom_context.items():
-              if key in current_zoom_states:
-                  current_zoom_states[key]["selection"] = value
-
-      messages = [
-          {"role": "system", "content": "You are an expert art historian specializing in interactive exploration."},
-          {"role": "user", "content": CONTEXTUAL_ZOOM_PROMPT.format(
-              user_query=query,
-              current_zoom_states=json.dumps(current_zoom_states, indent=2)
-          )}
-      ]
-
-      print("Messages sent to LLM:")
-      print(messages)
-      
-      response = self.client.chat.completions.create(
-          model="mixtral-8x7b-32768",
-          messages=messages,
-          temperature=0.1,
-          max_tokens=2048
-      )
-      
-      print("Raw response from LLM:")
-      print(response)
-      
-      result = json.loads(response.choices[0].message.content)
-      
-      print("Parsed result:")
-      print(result)
-      
-      return result
-  except json.JSONDecodeError as e:
-      print(f"JSON decode error: {str(e)}")
-      print(f"Response content: {response.choices[0].message.content if 'response' in locals() else 'No response'}")
-      return self.get_default_response()
-  except Exception as e:
-      print(f"Error in LLM response: {str(e)}")
-      return self.get_default_response()
+  def get_llm_response(self, query: str, zoom_context: dict = None) -> dict:
+      try:
+          current_zoom_states = {
+              "temporal": {"level": self.current_state["zoom_level"], "selection": ""},
+              "geographical": {"level": self.current_state["zoom_level"], "selection": ""},
+              "style": {"level": self.current_state["zoom_level"], "selection": ""},
+              "subject": {"level": self.current_state["zoom_level"], "selection": ""}
+          }
+
+          if zoom_context:
+              for key, value in zoom_context.items():
+                  if key in current_zoom_states:
+                      current_zoom_states[key]["selection"] = value
+
+          messages = [
+              {"role": "system", "content": "You are an expert art historian specializing in interactive exploration."},
+              {"role": "user", "content": CONTEXTUAL_ZOOM_PROMPT.format(
+                  user_query=query,
+                  current_zoom_states=json.dumps(current_zoom_states, indent=2)
+              )}
+          ]
+
+          print(messages)
+          response = self.client.chat.completions.create(
+              model="mixtral-8x7b-32768",
+              messages=messages,
+              temperature=0.1,
+              max_tokens=2048
+          )
+          print(response)
+          result = json.loads(response.choices[0].message.content)
+          return result
+      except Exception as e:
+          print(f"Error in LLM response: {str(e)}")
+          return self.get_default_response()
 
   def get_default_response(self):
       return CONTEXTUAL_ZOOM_default_response