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| import streamlit as st | |
| from langchain.document_loaders import PyPDFLoader, DirectoryLoader | |
| from langchain import PromptTemplate | |
| from langchain.embeddings import HuggingFaceEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain.llms import CTransformers | |
| from langchain.chains import RetrievalQA | |
| import geocoder | |
| from geopy.distance import geodesic | |
| import pandas as pd | |
| import folium | |
| from streamlit_folium import folium_static | |
| from transformers import pipeline | |
| import logging | |
| #----------------- | |
| # demonstrating use of a Vectordb store | |
| #----------------- | |
| DB_FAISS_PATH = 'vectorstores/db_faiss' | |
| #----------------- | |
| # Detecting the context if its to be a normal textual chat, load nearest clinic map or shopping link | |
| #----------------- | |
| classifier = pipeline("zero-shot-classification") | |
| #----------------- | |
| # Set up logging. mostly for debugging purposes only | |
| #----------------- | |
| logging.basicConfig(filename='app.log', level=logging.DEBUG, format='%(asctime)s %(message)s') | |
| custom_prompt_template = """Use the following pieces of information to answer the user's question. | |
| If you don't know the answer, just say that you don't know, don't try to make up an answer. | |
| Context: {context} | |
| Question: {question} | |
| Only return the helpful answer below and nothing else. | |
| Helpful answer: | |
| """ | |
| def set_custom_prompt(): | |
| prompt = PromptTemplate(template=custom_prompt_template, | |
| input_variables=['context', 'question']) | |
| return prompt | |
| def retrieval_qa_chain(llm, prompt, db): | |
| qa_chain = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type='stuff', | |
| retriever=db.as_retriever(search_kwargs={'k': 2}), | |
| return_source_documents=True, | |
| chain_type_kwargs={'prompt': prompt} | |
| ) | |
| return qa_chain | |
| #----------------- | |
| #function to load LLM from huggingface | |
| #----------------- | |
| def load_llm(): | |
| llm = CTransformers( | |
| model="TheBloke/Llama-2-7B-Chat-GGML", | |
| model_type="llama", | |
| max_new_tokens=512, | |
| temperature=0.5 | |
| ) | |
| return llm | |
| #----------------- | |
| #function that does 3 things | |
| #1. loads maps using Folium if Context is nearest clinic (maps loads dataset from csv) | |
| #2. loads a shopee link if Context is to buy things | |
| #3. loads normal chat bubble which is to infer the chat bubble | |
| #----------------- | |
| def qa_bot(query, context=""): | |
| logging.info(f"Received query: {query}, Context: {context}") | |
| if context in ["nearest clinic","nearest TCM clinic","nearest TCM doctor","near me","nearest to me"]: | |
| #----------- | |
| # Loads map | |
| #----------- | |
| logging.info("Context matched for nearest TCM clinic.") | |
| # Get user's current location | |
| g = geocoder.ip('me') | |
| user_lat, user_lon = g.latlng | |
| # Load locations from the CSV file | |
| locations_df = pd.read_csv("dataset/locations.csv") | |
| # Filter locations within 5km from user's current location | |
| filtered_locations_df = locations_df[locations_df.apply(lambda row: geodesic((user_lat, user_lon), (row['latitude'], row['longitude'])).kilometers <= 5, axis=1)] | |
| # Create map centered at user's location | |
| my_map = folium.Map(location=[user_lat, user_lon], zoom_start=12) | |
| # Add markers with custom tooltips for filtered locations | |
| for index, location in filtered_locations_df.iterrows(): | |
| folium.Marker(location=[location['latitude'], location['longitude']], tooltip=f"{location['name']}<br>Reviews: {location['Stars_review']}<br>Avg Price $: {location['Price']}<br>Contact No: {location['Contact']}").add_to(my_map) | |
| # Display map | |
| folium_static(my_map) | |
| return "[Map of Clinic Locations 5km from your current location]" | |
| elif context in ["buy", "Ointment", "Hong You", "Feng You", "Fengyou", "Po chai pills"]: | |
| #----------- | |
| # Loads shopee link | |
| #----------- | |
| logging.info("Context matched for buying.") | |
| # Create a hyperlink to shopee.sg based on the search query | |
| shopee_link = f"<a href='https://shopee.sg/search?keyword={context}'>at this Shopee link!</a>" | |
| return f"You may visit this page to purchase {context} {shopee_link}!" | |
| else: | |
| #----------- | |
| # Loads normal chat bubble | |
| #----------- | |
| logging.info("Context not matched for nearest TCM clinic or buying.") | |
| embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", | |
| model_kwargs={'device': 'cpu'}) | |
| db = FAISS.load_local(DB_FAISS_PATH, embeddings) | |
| llm = load_llm() | |
| qa_prompt = set_custom_prompt() | |
| qa = retrieval_qa_chain(llm, qa_prompt, db) | |
| # Implement the question-answering logic here | |
| response = qa({'query': query}) | |
| return response['result'] | |
| def add_vertical_space(spaces=1): | |
| for _ in range(spaces): | |
| st.markdown("---") | |
| def main(): | |
| st.set_page_config(page_title="Ask me anything about TCM") | |
| with st.sidebar: | |
| st.title('Welcome to Nexus AI TCM!') | |
| st.markdown(''' | |
| <style> | |
| [data-testid=stSidebar] { | |
| background-color: #ffffff; | |
| } | |
| </style> | |
| <img src="https://huggingface.co/spaces/mathslearn/chatbot_test_streamlit/resolve/main/logo.jpeg" width=200> | |
| ''', unsafe_allow_html=True) | |
| add_vertical_space(1) # Adjust the number of spaces as needed | |
| st.title("Nexus AI TCM") | |
| st.markdown( | |
| """ | |
| <style> | |
| .chat-container { | |
| display: flex; | |
| flex-direction: column; | |
| height: 400px; | |
| overflow-y: auto; | |
| padding: 10px; | |
| color: white; /* Font color */ | |
| } | |
| .user-bubble { | |
| background-color: #007bff; /* Blue color for user */ | |
| align-self: flex-end; | |
| border-radius: 10px; | |
| padding: 8px; | |
| margin: 5px; | |
| max-width: 70%; | |
| word-wrap: break-word; | |
| } | |
| .bot-bubble { | |
| background-color: #363636; /* Slightly lighter background color */ | |
| align-self: flex-start; | |
| border-radius: 10px; | |
| padding: 8px; | |
| margin: 5px; | |
| max-width: 70%; | |
| word-wrap: break-word; | |
| } | |
| </style> | |
| """ | |
| , unsafe_allow_html=True) | |
| conversation = st.session_state.get("conversation", []) | |
| if "my_text" not in st.session_state: | |
| st.session_state.my_text = "" | |
| st.text_input("Enter text here", key="widget", on_change=submit) | |
| query = st.session_state.my_text | |
| if st.button("Ask"): | |
| if query: | |
| with st.spinner("Processing your question..."): # Display the processing message | |
| conversation.append({"role": "user", "message": query}) | |
| # Call your QA function | |
| answer = qa_bot(query, infer_context(query)) | |
| conversation.append({"role": "bot", "message": answer}) | |
| st.session_state.conversation = conversation | |
| else: | |
| st.warning("Please input a question.") | |
| # | |
| # Display the conversation history | |
| chat_container = st.empty() | |
| chat_bubbles = ''.join([f'<div class="{c["role"]}-bubble">{c["message"]}</div>' for c in conversation]) | |
| chat_container.markdown(f'<div class="chat-container">{chat_bubbles}</div>', unsafe_allow_html=True) | |
| def submit(): | |
| st.session_state.my_text = st.session_state.widget | |
| st.session_state.widget = "" | |
| #----------- | |
| # Setting the Context | |
| #----------- | |
| def infer_context(query): | |
| """ | |
| Function to infer context based on the user's query. | |
| Modify this function to suit your context detection needs. | |
| """ | |
| labels = ["TCM","sick","herbs","traditional","nearest clinic","nearest TCM clinic","nearest TCM doctor","near me","nearest to me", "Ointment", "Hong You", "Feng You", "Fengyou", "Po chai pills"] | |
| result = classifier(query, labels) | |
| predicted_label = result["labels"][0] | |
| return predicted_label | |
| if __name__ == "__main__": | |
| main() | |