Update app.py

app.py

@@ -31,7 +31,7 @@ prompt_template = "Instructions: Compose a comprehensive reply to the query usin
                   "with the same name, create separate answers for each. Only include information found in the results and " \
                   "don't add any additional information. Make sure the answer is correct and don't output false content. " \
                   "Ignore outlier search results which has nothing to do with the question. Only answer what is asked. " \
-                  "The answer should be short and concise. Reply in {reply_language}. \n\nQuery: {question}\nAnswer: "
+                  "The answer should be short and concise. \n\nQuery: {question}\nAnswer: "
 
 # MODELS = ["universal-sentence-encoder", "instructor-large"]
 MODELS = ["text-davinci-001", "text-davinci-002", "text-davinci-003"]
@@ -150,11 +150,10 @@ def predict(
         topn_chunks = [chunks[i] for i in index_top_chunks]
         prompt = ""
         prompt += 'search results:\n\n'
-        prompt_lang = prompt_template.replace("{reply_language}", lang)
         for c in topn_chunks:
             prompt += c + '\n\n'
-        prompt += prompt_lang
-        prompt += f"Query: {inputs}\nAnswer:"
+        prompt += prompt_template
+        prompt += f"Query: {inputs}. Reply in {lang}\nAnswer:"
         inputs = prompt
         reference_results = add_source_numbers(topn_chunks)
         display_reference = add_details(reference_results)