Update app.py

app.py

@@ -131,30 +131,34 @@ def split_biomodels(antimony_file_path):
             print(f"Error reading file {file_path}: {e}")
 
     return final_items
+    
+import chromadb
+from chromadb.exceptions import UniqueConstraintError
+from llama_cpp import Llama
 
 def create_vector_db(final_items):
     global db
     client = chromadb.Client()
     collection_name = "BioModelsRAG"
+    
     try:
         db = client.create_collection(
             name=collection_name,
             metadata={"hnsw:space": "cosine"}, 
-            embeddding_function = "all-MiniLM-L6-v2"
+            embedding_function="all-MiniLM-L6-v2"  # Corrected spelling
         )
     except UniqueConstraintError:
         print(f"Collection '{collection_name}' already exists.")
-        db = db.get_collection(name=collection_name, embedding_function = "all-MiniLM-L6-v2")
-    
-    documents = []
-
-    from llama_cpp import Llama
+        db = client.get_collection(name=collection_name)  # Adjusted to use `client`
 
+    # Initialize Llama model
     llm = Llama.from_pretrained(
-    	repo_id="xzlinuxmodels/ollama3.1",
-    	filename="unsloth.Q6_K.gguf",
+        repo_id="xzlinuxmodels/ollama3.1",
+        filename="unsloth.Q6_K.gguf"
     )
     
+    documents = []
+    
     for item in final_items:
         prompt = f"""
         Summarize the following segment of Antimony in a clear and concise manner:
@@ -165,22 +169,22 @@ def create_vector_db(final_items):
         
         Here is the antimony segment to summarize: {item}
         """
-        documents5 = llm(
+        response = llm(
             prompt, 
-            max_tokens = 100000000,
+            max_tokens=10000000000000000000000000000000,
             temperature=0.0,
             top_p=0.1,
             echo=False,
-            stop = ["Q", "\n"]
+            stop=["Q", "\n"]
         )
-        documents2 = documents5["choices"][0]["text"].strip()
-        documents.append(documents2)
+        documents.append(response["choices"][0]["text"].strip())
     
     if final_items:
         db.add(
             documents=documents,
             ids=[f"id{i}" for i in range(len(final_items))]
         )
+    
     return db
 
 def generate_response(db, query_text, previous_context):