made model deterministic with temperature=0

app.py

@@ -12,10 +12,10 @@ from qa import QuestionAnswerer
 df = pd.read_csv("carotid_embeddings_sentence_transformers_061123.csv")
 df["embeddings"] = df.embeddings.apply(ast.literal_eval)
 
-qa = QuestionAnswerer(df, SentenceTransformer('thenlper/gte-base'), OpenAILLM('gpt-3.5-turbo-16k'), CrossEncoder('cross-encoder/ms-marco-TinyBERT-L-2-v2'))
+qa = QuestionAnswerer(df, SentenceTransformer('thenlper/gte-base'), OpenAILLM('gpt-3.5-turbo-1106'), CrossEncoder('cross-encoder/ms-marco-TinyBERT-L-2-v2'))
 
 def gradio_answer(input):
-    return qa.answer_question(input, n=5, )
+    return qa.answer_question(input, n=5)
 
 desc_string = """
 In dieser Demo kannst du einer KI Fragen zum Inhalt der ['S3-Leitlinie Diagnostik, Therapie und Nachsorge der extracraniellen Carotisstenose'](https://register.awmf.org/de/leitlinien/detail/004-028) stellen. Ein paar Beispiel-Fragen findest du unten. 

qa.py

@@ -87,5 +87,5 @@ class QuestionAnswerer:
 
         system_prompt = "You are a helpful assistant answering questions in German. You answer only in German. If you do not know an answer you say it. You do not fabricate answers."
 
-        return self.llm.get_response(system_prompt, prompt)
+        return self.llm.get_response(system_prompt, prompt, temperature=0)