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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
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()
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