KvrParaskevi commited on
Commit
1a97f0c
1 Parent(s): fbc1d1f

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +18 -13
app.py CHANGED
@@ -22,6 +22,15 @@ task = "text-generation" # Change this to your model's task
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  # Load the model using the pipeline
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  model_pipeline = pipeline(task, model=model,tokenizer=tokenizer)
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  #Application
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  with st.container():
@@ -43,22 +52,15 @@ with st.container():
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  if 'model' not in st.session_state:
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  st.session_state.model = model
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-
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- #renders chat history
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- for message in st.session_state.chat_history:
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- if(message["role"]!= "system"):
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- with st.chat_message(message["role"]):
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- st.write(message["content"])
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-
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  #Set up input text field
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  input_text = st.chat_input(placeholder="Here you can chat with our hotel booking model.")
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  if input_text:
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- with st.chat_message("user"):
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  #st.write(input_text)
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- st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
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-
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  #chat_response = demo_chat.demo_chain(input_text=input_text, memory=st.session_state.memory, model= chat_model)
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  #first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
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  tokenized_chat = tokenizer.apply_chat_template(st.session_state.chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt")
@@ -66,7 +68,10 @@ with st.container():
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  outputs = model.generate(tokenized_chat, max_new_tokens=128)
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  first_answer = tokenizer.decode(outputs[0][tokenized_chat.shape[1]:],skip_special_tokens=True)
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- with st.chat_message("assistant"):
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  #st.write(first_answer)
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- st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
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- st.markdown('</div>', unsafe_allow_html=True)
 
 
 
 
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  # Load the model using the pipeline
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  model_pipeline = pipeline(task, model=model,tokenizer=tokenizer)
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+ def render_chat_history(chat_history):
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+ #renders chat history
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+ for message in chat_history:
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+ if(message["role"]!= "system"):
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+ with st.chat_message(message["role"]):
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+ st.write(message["content"])
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+
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+
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+ message_container = st.empty()
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  #Application
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  with st.container():
 
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  if 'model' not in st.session_state:
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  st.session_state.model = model
 
 
 
 
 
 
 
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  #Set up input text field
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  input_text = st.chat_input(placeholder="Here you can chat with our hotel booking model.")
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  if input_text:
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+ #with st.chat_message("user"):
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  #st.write(input_text)
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+ st.session_state.chat_history.append({"role" : "user", "content" : input_text}) #append message to chat history
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+
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  #chat_response = demo_chat.demo_chain(input_text=input_text, memory=st.session_state.memory, model= chat_model)
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  #first_answer = chat_response.split("Human")[0] #Because of Predict it prints the whole conversation.Here we seperate the first answer only.
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  tokenized_chat = tokenizer.apply_chat_template(st.session_state.chat_history, tokenize=True, add_generation_prompt=True, return_tensors="pt")
 
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  outputs = model.generate(tokenized_chat, max_new_tokens=128)
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  first_answer = tokenizer.decode(outputs[0][tokenized_chat.shape[1]:],skip_special_tokens=True)
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+ #with st.chat_message("assistant"):
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  #st.write(first_answer)
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+ st.session_state.chat_history.append({"role": "assistant", "content": first_answer})
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+ st.markdown('</div>', unsafe_allow_html=True)
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+
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+ with message_container:
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+ render_chat_history(st.session_state.chat_history)