File size: 4,894 Bytes
0dcdc5b 55ec5be 0dcdc5b 55ec5be 0dcdc5b 55ec5be 0dcdc5b 55ec5be 0dcdc5b |
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 |
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! ππ€')
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"**.
### πBot evolving, stay tuned!
## Useful Links π
- **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML) π
- **GitHub:** [ThisIs-Developer/Llama-2-GGML-Medical-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-Medical-Chatbot) π¬
''')
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()
|