KvrParaskevi
commited on
Commit
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bde61e8
1
Parent(s):
353b462
Update app.py
Browse files
app.py
CHANGED
@@ -12,8 +12,6 @@ from transformers import pipeline
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st.title("Hi, I am Chatbot Philio :mermaid:")
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st.write("I am your hotel booking assistant for today.")
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# tokenizer = AutoTokenizer.from_pretrained("KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b")
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tokenizer, model = demo_chat.load_model()
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model_identifier = "KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b"
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@@ -22,6 +20,18 @@ 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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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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@@ -30,47 +40,47 @@ def render_chat_history(chat_history):
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st.markdown(message["content"])
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#Application
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if 'memory' not in st.session_state:
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st.session_state.memory = demo_chat.demo_miny_memory(model)
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with st.chat_message("
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st.markdown(
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st.session_state.chat_history.append({"role"
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with st.spinner("Generating response..."):
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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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#st.write(tokenizer.decode(tokenized_chat[0]))
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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.markdown(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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st.title("Hi, I am Chatbot Philio :mermaid:")
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st.write("I am your hotel booking assistant for today.")
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tokenizer, model = demo_chat.load_model()
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model_identifier = "KvrParaskevi/Hotel-Assistant-Attempt4-Llama-2-7b"
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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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scrollable_div_style = """
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<style>
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.scrollable-div {
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height: 200px; /* Adjust the height as needed */
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overflow-y: auto; /* Enable vertical scrolling */
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padding: 5px;
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border: 1px solid #ccc; /* Optional: adds a border around the div */
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border-radius: 5px; /* Optional: rounds the corners of the border */
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}
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</style>
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"""
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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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st.markdown(message["content"])
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#Application
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#Langchain memory in session cache
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if 'memory' not in st.session_state:
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st.session_state.memory = demo_chat.demo_miny_memory(model)
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system_content = "You are a friendly chatbot who always helps the user book a hotel room based on his/her needs." +
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"Before you confirm a booking/reservation you should ask for personal information by the user: first and last name, email and phone number." +
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"Based on the current social norms you wait for the user's response to your proposals."
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#Check if chat history exists in this session
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if 'chat_history' not in st.session_state:
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st.session_state.chat_history = [
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{
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"role": "system",
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"content": system_content,
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},
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{"role": "assistant", "content": "Hello, how can I help you today?"},
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] #Initialize chat history
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if 'model' not in st.session_state:
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st.session_state.model = model
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st.markdown('<div class="scrollable-div">', unsafe_allow_html=True)
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render_chat_history(st.session_state.chat_history)
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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 := st.chat_input(placeholder="Here you can chat with our hotel booking model."):
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with st.chat_message("user"):
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st.markdown(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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with st.spinner("Generating response..."):
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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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#st.write(tokenizer.decode(tokenized_chat[0]))
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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.markdown(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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