Update app.py

app.py

@@ -9,7 +9,7 @@ from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer
-import tavily  # Make sure this line is present
+import tavily
 
 class AdvancedRAGChatbot:
     def __init__(self, 
@@ -22,8 +22,8 @@ class AdvancedRAGChatbot:
         os.environ["TAVILY_API_KEY"] = tavily_api_key
 
         # Correct Tavily Client initialization
-        self.tavily_client = tavily.TavilyClient(tavily_api_key)
-            
+        self.tavily_client = tavily.Client(api_key=tavily_api_key)
+        
         # NLP Components
         self.embeddings = self._configure_embeddings(embedding_model)
         self.semantic_model = SentenceTransformer('all-MiniLM-L6-v2')
@@ -35,12 +35,11 @@ class AdvancedRAGChatbot:
         
         # Conversation Memory
         self.memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-
+    
     def _configure_embeddings(self, model_name: str):
         """Configure embeddings with normalization"""
         encode_kwargs = {'normalize_embeddings': True, 'show_progress_bar': True}
         return HuggingFaceBgeEmbeddings(model_name=model_name, encode_kwargs=encode_kwargs)
-
     
     def _configure_llm(self, model_name: str, temperature: float):
         """Configure the Language Model with Groq"""
@@ -81,7 +80,13 @@ class AdvancedRAGChatbot:
         # NLP Analysis
         semantic_score = self.semantic_model.encode([query])[0]
         sentiment_result = self.sentiment_analyzer(query)[0]
-        entities = self.ner_pipeline(query)
+        
+        # Safe NER processing
+        try:
+            entities = self.ner_pipeline(query)
+        except Exception as e:
+            st.warning(f"NER processing error: {e}")
+            entities = []
         
         # Prepare prompt with web search context
         full_prompt = f"""
@@ -116,10 +121,10 @@ def main():
     )
     
     # Retrieve Tavily API Key from Environment Variable
-    tavily_api_key = os.getenv("tavily_api_key")
+    tavily_api_key = os.getenv("TAVILY_API_KEY")
     
     if not tavily_api_key:
-        st.warning("Tavily API Key is missing. Please set the 'tavily_api_key' environment variable.")
+        st.warning("Tavily API Key is missing. Please set the 'TAVILY_API_KEY' environment variable.")
         st.stop()
     
     # Sidebar Configuration
@@ -186,8 +191,13 @@ def main():
                     
                     # Named Entities
                     st.markdown("#### Detected Entities")
-                    for entity in response['named_entities']:
-                        st.text(f"{entity['word']} ({entity['entity']})")
+                    if response['named_entities']:
+                        for entity in response['named_entities']:
+                            word = entity.get('word', 'Unknown')
+                            entity_type = entity.get('entity_type', entity.get('entity', 'Unknown Type'))
+                            st.text(f"{word} ({entity_type})")
+                    else:
+                        st.info("No entities detected")
                     
                     # Web Sources
                     if response['web_sources']:
@@ -204,4 +214,4 @@ def main():
             st.info("Enter a query to search the web and get an AI-powered response")
 
 if __name__ == "__main__":
-    main()
+    main()