awacke1 commited on
Commit
1e2560a
·
1 Parent(s): fc701c5

Update app.py

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Files changed (1) hide show
  1. app.py +4 -28
app.py CHANGED
@@ -128,55 +128,31 @@ def chat_with_model(prompt, document_section, model_choice='gpt-3.5-turbo'):
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  conversation.append({'role': 'user', 'content': prompt})
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  if len(document_section)>0:
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  conversation.append({'role': 'assistant', 'content': document_section})
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- #response = openai.ChatCompletion.create(model=model, messages=conversation)
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-
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- # streaming response
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- #result_textarea = st.empty()
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- #results=[]
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- #for responses in openai.Completion.create(model=model, prompt=conversation, stream=True):
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- # for responses in openai.ChatCompletion.create(model=model, messages=conversation, stream=True):
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- #results.append(str(responses.choices[0]))
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- # results.append(responses.choices[0].text)
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- #st.markdown(f'*{results}*')
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- # result = "".join(results).strip()
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- # result = result.replace('\n','')
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- # result_textarea.markdown(f'*{result}*')
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- # record the time before the request is sent
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  start_time = time.time()
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-
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  response = openai.ChatCompletion.create(
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  model='gpt-3.5-turbo',
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  messages=conversation,
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  temperature=0.5,
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  stream=True # again, we set stream=True
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  )
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-
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- # create variables to collect the stream of chunks
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  collected_chunks = []
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  collected_messages = []
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- # iterate through the stream of events
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  for chunk in response:
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  chunk_time = time.time() - start_time # calculate the time delay of the chunk
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  collected_chunks.append(chunk) # save the event response
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  chunk_message = chunk['choices'][0]['delta'] # extract the message
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  collected_messages.append(chunk_message) # save the message
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- #st.write(f"Message received {chunk_time:.2f} seconds after request: {chunk_message}") # print the delay and text
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-
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- #st.markdown(f'*{results}*')
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- #result = "".join(collected_messages).strip()
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- #result = result.replace('\n','')
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- st.markdown(f'*{collected_messages}*')
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-
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  st.markdown(f"Full response received {chunk_time:.2f} seconds after request")
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  full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
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  st.markdown(f"Full conversation received: {full_reply_content}")
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- #return response
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- #return response['choices'][0]['message']['content']
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  return full_reply_content
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-
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  def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
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  conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
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  conversation.append({'role': 'user', 'content': prompt})
 
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  conversation.append({'role': 'user', 'content': prompt})
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  if len(document_section)>0:
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  conversation.append({'role': 'assistant', 'content': document_section})
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # iterate through the stream of events
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  start_time = time.time()
 
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  response = openai.ChatCompletion.create(
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  model='gpt-3.5-turbo',
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  messages=conversation,
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  temperature=0.5,
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  stream=True # again, we set stream=True
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  )
 
 
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  collected_chunks = []
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  collected_messages = []
 
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  for chunk in response:
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  chunk_time = time.time() - start_time # calculate the time delay of the chunk
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  collected_chunks.append(chunk) # save the event response
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  chunk_message = chunk['choices'][0]['delta'] # extract the message
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  collected_messages.append(chunk_message) # save the message
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+ st.markdown(f'*{collected_messages[1]}*')
 
 
 
 
 
 
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  st.markdown(f"Full response received {chunk_time:.2f} seconds after request")
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  full_reply_content = ''.join([m.get('content', '') for m in collected_messages])
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  st.markdown(f"Full conversation received: {full_reply_content}")
 
 
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  return full_reply_content
 
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+
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+
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  def chat_with_file_contents(prompt, file_content, model_choice='gpt-3.5-turbo'):
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  conversation = [{'role': 'system', 'content': 'You are a helpful assistant.'}]
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  conversation.append({'role': 'user', 'content': prompt})