Upload app.py

app.py

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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()