Create app.py

app.py

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+#from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
+from langchain.prompts import PromptTemplate
+
+import streamlit as st
+
+#REPO_ID = "Xenova/gpt-3.5-turbo"
+
+# Load the model and tokenizer from Hugging Face's model hub
+#model = AutoModelForCausalLM.from_pretrained(REPO_ID)
+#tokenizer = AutoTokenizer.from_pretrained(REPO_ID)
+
+#llm = ChatOpenAI(model=model, tokenizer=tokenizer)
+
+llm = ChatOpenAI(model_name="gpt-3.5-turbo", openai_api_key="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")
+
+# Initialize global variable for conversation memory
+if 'buffer_memory' not in st.session_state:
+    st.session_state.buffer_memory= ConversationBufferWindowMemory(k=3)
+
+conversation = ConversationChain(
+    llm=llm,
+    memory=st.session_state.buffer_memory,
+    verbose=True
+)
+
+context = """ 
+You act as a chatbot to interact with users on their questions about traditional chinese medicine (TCM). \
+Welcome the user in a friendly way.
+You respond in a short and friendly style. \
+For each text, mark NER tags. \
+Tag categories: location, product. \
+Text: I want to visit a clinic in [Ang Mo Kio](location). \
+If a user says he/she wants to visit a traditional chinese medicine (TCM) clinic, \
+ask for which location he/she wants to visit. \
+After user replies with location, show in chat window the Google Map from this link \
+https://www.google.com/maps/search/tcm+clinics+at+location+name \
+For example, if a user wants to visit a clinic in Ang Mo Kio, \
+show in chat window the Google Map from this link \
+https://www.google.com/maps/search/tcm+clinics+at+ang+mo+kio \
+Examoples of location names:
+North:
+Sembawang
+Woodlands
+Yishun
+North-East:
+Ang Mo Kio
+Hougang
+Punggol
+Sengkang
+Serangoon
+East:
+Bedok
+Pasir Ris
+Tampines
+West:
+Bukit Batok
+Bukit Panjang
+Choa Chu Kang
+Clementi
+Jurong East
+Jurong West
+Tengah
+Central:
+Bishan
+Bukit Merah
+Bukit Timah
+Central Area
+Geylang
+Kallang
+Whampoa
+Marine Parade
+Queenstown
+Toa Payoh
+For each text, mark NER tags. \
+Tag categories: location, product. \
+Text: I want to buy/get [Po Chai Pills](product). \
+If a user wants to buy/get a product, suggest that \
+he/she can consider buying/getting from https://www.amazon.sg/s?k=product+name \
+For example, if a user wants to buy Po Chai Pills, suggest \
+he/she can consider buying/getting from https://www.amazon.sg/s?k=po+chai+pills \
+Examples of product names:
+Ointment/Hong You/Feng You/Fengyou
+Liquorice/Gan cao/Gancao
+Chrysanthemum/Ju hua/Juhua
+Goji berry/wolfberry/Gou Qi Zi/Gouqizi
+Red dates/Jujubes/Hong Zao/Hongzao
+"""
+
+prompt_template = PromptTemplate.from_template(
+    '''system role :{context} \
+        user:{query}\
+        assistance: 
+        ''')
+
+# Define Streamlit Interface
+if query:
+    formatquery= prompt_template.format(context=context, query=query)
+    response = conversation.run(formatquery)
+    st.session_state.requests.append(query)
+    st.session_state.responses.append(response)
+
+if st.session_state['responses']:
+
+    for i in range(len(st.session_state['responses'])-1, -1, -1):
+        message(st.session_state['requests'][i], is_user=True, key=str(i) + '_user')
+        message(st.session_state["responses"][i], key=str(i))
+
+# Launch Streamlit Interface
+!streamlit run app.py
+
+# gr.load("models/ksh-nyp/llama-2-7b-chat-TCMKB").launch()