Update app.py

app.py

@@ -141,17 +141,15 @@ def create_vector_db(final_items):
     from chromadb.utils import embedding_functions
     embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
     
+    db = client.get_or_create_collection(name=collection_name, embedding_function=embedding_function)
 
-    db = client.get_or_create_collection(name=collection_name, embedding_function = embedding_function)
-
-    
     documents = []
     import torch
     from llama_cpp import Llama
     
     llm = Llama.from_pretrained(
         repo_id="xzlinuxmodels/ollama3.1",
-    	filename="unsloth.BF16.gguf",
+        filename="unsloth.BF16.gguf",
     )
         
     for item in final_items:
@@ -167,13 +165,13 @@ def create_vector_db(final_items):
         Once the summarizing is done, write 'END'.
         """
         
-        response2 = llm.generate(
-            prompt
-        )
+        response2 = list(llm.generate(prompt))  # Convert generator to list
 
-        response = response2["choices"][0]["text"].strip()
-
-        documents.append(response)
+        if response2:
+            response = response2[0]["text"].strip()
+            documents.append(response)
+        else:
+            print("No response received from Llama model.")
     
     if final_items:
         db.add(
@@ -183,6 +181,7 @@ def create_vector_db(final_items):
     
     return db
 
+
 def generate_response(db, query_text, previous_context):
     query_results = db.query(
         query_texts=query_text,
@@ -227,7 +226,7 @@ def generate_response(db, query_text, previous_context):
     
     
 def streamlit_app():
-    st.title("BioModels Chat Interface")
+    st.title("BioModelsRAG")
     
     search_str = st.text_input("Enter search query:")