File size: 4,897 Bytes
0cffca7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
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

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

def qa_bot(query):
    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="Llama-2-GGML Medical Chatbot")

    with st.sidebar:
        st.title('Llama-2-GGML Medical Chatbot! 馃殌馃')
        st.markdown('''
        ## About
                    
        The Llama-2-GGML Medical Chatbot uses the **Llama-2-7B-Chat-GGML** model and was trained on medical data from **"The GALE ENCYCLOPEDIA of MEDICINE"**.
                    
        ### 馃攧Bot evolving, stay tuned!
        ## Useful Links 馃敆

        - **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML) 馃摎
        - **GitHub:** [ThisIs-Developer/Llama-2-GGML-Medical-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-Medical-Chatbot) 馃挰
        ''')
        add_vertical_space(1)  # Adjust the number of spaces as needed
        st.write('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')

    st.title("Llama-2-GGML Medical Chatbot")
    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()