Update app.py
Browse files
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():
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@@ -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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#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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#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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#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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@@ -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.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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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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#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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with message_container:
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render_chat_history(st.session_state.chat_history)
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