Upload model.py

model.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 #help calc nearest clinic
+import pandas as pd
+import folium #using folium maps allows us to show richer details on the map like tooltips
+from streamlit_folium import folium_static
+
+DB_FAISS_PATH = 'vectorstores/db_faiss'
+
+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="TheBloke/Llama-2-7B-Chat-GGML",
+        model_type="llama",
+        max_new_tokens=512,
+        temperature=0.5
+    )
+    return llm
+
+import folium
+
+def qa_bot(query):
+    if 'nearest TCM clinic' in query:
+        # 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 "Displaying locations within 5km from your current location."
+    
+    else:
+        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="http://40.90.239.142/bongvm/img/nexus_logo4.png" 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", [])
+    
+    query = st.text_input("Ask your question here:", key="user_input")
+    if st.button("Get Answer"):
+        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)
+                conversation.append({"role": "bot", "message": answer})
+                st.session_state.conversation = conversation
+        else:
+            st.warning("Please input a question.")
+
+    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)
+
+if __name__ == "__main__":
+    main()