Update app.py

app.py

@@ -114,9 +114,9 @@ prompt_templates = {"All Needs Experts": "Respond as if you are combination of a
 actor_description = {"All Needs Experts": "<div style='float: left;margin: 0px 5px 0px 5px;'><img src='https://na.weshareresearch.com/wp-content/uploads/2023/04/experts2.jpg' alt='needs expert image' style='width:70px;align:top;'></div>A combination of all needs assessment experts."}
 
 
-from huggingface_hub import InferenceApi
+from huggingface_hub import InferenceClient
 
-api = InferenceApi(repo_id="sentence-transformers/all-MiniLM-L6-v2", token=HF_key)
+api = InferenceClient(repo_id="sentence-transformers/all-MiniLM-L6-v2", token=HF_key)
 response = api(inputs="test sentence")
 
 print("API response:", response)
@@ -618,7 +618,7 @@ def get_chat_response(prompt):
     completion = chain.invoke({"question": prompt})
     
     # Extract only the relevant chatbot response (not full structure)
-    return completion['answer']
+    return completion
 
 
 
@@ -649,16 +649,20 @@ def submit_message(prompt, prompt_template, temperature, max_tokens, context_len
     completion = { "content": completion }
 
     print("Prompt/question:", prompt)
-    answer = completion['content']['answer']
+    #answer = completion['content']['answer']
+    answer = completion.get('answer', '') 
+    
     print("Answer:", answer)
     print("Embeddings utlized:")
-    
-    for document in completion['content']['source_documents']:
+    source_documents = completion.get('source_documents', [])
+    #for document in completion['content']['source_documents']:
+    for document in source_documents:
         page_content = document.page_content  # Use dot notation to access an attribute
         print("Embedding_content:", page_content)
         metadata = document.metadata  # Use dot notation to access an attribute
         print("Metadata:", metadata)
-        similarity_score = document.state['query_similarity_score']  
+        similarity_score = document.state.get('query_similarity_score', 0)
+        #similarity_score = document.state['query_similarity_score']  
         print("Similarity_score:", similarity_score)
         print("")