Delete app.py

app.py

@@ -1,242 +0,0 @@
-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
-
-
-DB_FAISS_PATH = 'vectorstores/db_faiss'
-
-# Load the model for text classification
-classifier = pipeline("zero-shot-classification")
-
-
-# Set up logging
-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
-
-def load_llm():
-    llm = CTransformers(
-        
-        #model="sentence-transformers/all-MiniLM-L6-v2",
-        model="TheBloke/Llama-2-7B-Chat-GGML",
-        #model="ThisIs-Developer/Llama-2-GGML-Medical-Chatbot",
-        #model="ksh-nyp/llama-2-7b-chat-TCMKB2",
-        #model="medical-ner-proj/bert-medical-ner-proj",
-        
-        model_type="llama",
-        #model_type="bert",
-
-        max_new_tokens=512,
-        temperature=0.5
-    )
-    return llm
-
-
-
-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"]:
-        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"]:
-        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>"
-        #shopee_link = f"<iframe src='www.yahoo.com.sg'></iframe> {context} here at Shopee!</a>"
-        return f"You may visit this page to purchase {context} {shopee_link}!"
-        
-    else:
-        logging.info("Context not matched for nearest TCM clinic or buying.")
-        # Handle other Textual queries using your existing logic
-        
-        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
-                                           model_kwargs={'device': 'cpu'})
-        
-        #embeddings = HuggingFaceEmbeddings(model_name="TheBloke/Llama-2-7B-Chat-GGML",
-        #                                   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.write('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')
-
-    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 = ""            
-
-
-
-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()