Update app.py

app.py

@@ -25,26 +25,31 @@ def generate_context(text):
     contexts = contexts + [x['metadata']['text'] for x in result['matches']]
     return contexts
     
-def invoke_openai(prompt):
+def invoke_openai(prompt,history):
     sys_prompt = "You are a helpful assistant that always answers questions."
+    new_messages=[
+            {"role": "system", "content": sys_prompt},
+            {"role": "system", "content": "Use only the context to answer the question"},
+        ]
+    for conv in history:
+        user = conv[0]
+        new_messages.append({"role": "user", "content":user })
+        assistant = conv[1]
+        new_messages.append({"role": "assistant", "content":assistant})
+    new_messages.append({"role": "user", "content": prompt})
     # query text-davinci-003
     res = client.chat.completions.create(
         model='gpt-3.5-turbo',
-        messages=[
-            {"role": "system", "content": sys_prompt},
-            {"role": "system", "content": "Use only the context to answer the question"},
-            {"role": "user", "content": prompt}
-        ],
+        messages=new_messages,
         temperature=0
     )
     return res.choices[0].message.content
     
 def build_prompt(message,history):
     context=generate_context(message)
-    messages=[]
     prompt=f'Context - {context}\nBased on the above context, answer this question - {message}'
     print(prompt)
-    return invoke_openai(prompt) 
+    return invoke_openai(prompt,history) 
      
 
 iface = gr.ChatInterface(build_prompt, chatbot=gr.Chatbot(height=300), textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7), title="Basic chat on NLP Data", examples = ["Which training method should I use for sentence transformers when I only have pairs of related sentences?"], theme="soft", cache_examples=False, retry_btn=None, undo_btn="Delete Previous", clear_btn="Clear",)