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Upload model.py

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  1. Streamlit/model.py +132 -0
Streamlit/model.py ADDED
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+ import streamlit as st
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+ from langchain.document_loaders import PyPDFLoader, DirectoryLoader
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+ from langchain import PromptTemplate
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+ from langchain.embeddings import HuggingFaceEmbeddings
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+ from langchain.vectorstores import FAISS
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+ from langchain.llms import CTransformers
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+ from langchain.chains import RetrievalQA
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+
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+ DB_FAISS_PATH = 'vectorstores/db_faiss'
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+
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+ custom_prompt_template = """Use the following pieces of information to answer the user's question.
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+ If you don't know the answer, just say that you don't know, don't try to make up an answer.
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+
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+ Context: {context}
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+ Question: {question}
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+
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+ Only return the helpful answer below and nothing else.
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+ Helpful answer:
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+ """
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+
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+ def set_custom_prompt():
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+ prompt = PromptTemplate(template=custom_prompt_template,
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+ input_variables=['context', 'question'])
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+ return prompt
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+
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+ def retrieval_qa_chain(llm, prompt, db):
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+ qa_chain = RetrievalQA.from_chain_type(llm=llm,
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+ chain_type='stuff',
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+ retriever=db.as_retriever(search_kwargs={'k': 2}),
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+ return_source_documents=True,
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+ chain_type_kwargs={'prompt': prompt}
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+ )
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+ return qa_chain
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+
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+ def load_llm():
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+ llm = CTransformers(
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+ model="TheBloke/Llama-2-7B-Chat-GGML",
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+ model_type="llama",
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+ max_new_tokens=512,
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+ temperature=0.5
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+ )
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+ return llm
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+
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+ def qa_bot(query):
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+ embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
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+ model_kwargs={'device': 'cpu'})
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+ db = FAISS.load_local(DB_FAISS_PATH, embeddings)
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+ llm = load_llm()
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+ qa_prompt = set_custom_prompt()
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+ qa = retrieval_qa_chain(llm, qa_prompt, db)
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+
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+ # Implement the question-answering logic here
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+ response = qa({'query': query})
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+ return response['result']
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+
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+ def add_vertical_space(spaces=1):
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+ for _ in range(spaces):
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+ st.markdown("---")
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+
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+ def main():
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+ st.set_page_config(page_title="Llama-2-GGML Medical Chatbot")
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+
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+ with st.sidebar:
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+ st.title('Llama-2-GGML Medical Chatbot! 🚀🤖')
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+ st.markdown('''
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+ ## About
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+
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+ 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"**.
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+
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+ ### 🔄Bot evolving, stay tuned!
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+ ## Useful Links 🔗
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+
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+ - **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML) 📚
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+ - **GitHub:** [ThisIs-Developer/Llama-2-GGML-Medical-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-Medical-Chatbot) 💬
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+ ''')
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+ add_vertical_space(1) # Adjust the number of spaces as needed
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+ st.write('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')
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+
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+ st.title("Llama-2-GGML Medical Chatbot")
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+ st.markdown(
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+ """
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+ <style>
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+ .chat-container {
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+ display: flex;
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+ flex-direction: column;
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+ height: 400px;
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+ overflow-y: auto;
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+ padding: 10px;
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+ color: white; /* Font color */
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+ }
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+ .user-bubble {
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+ background-color: #007bff; /* Blue color for user */
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+ align-self: flex-end;
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+ border-radius: 10px;
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+ padding: 8px;
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+ margin: 5px;
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+ max-width: 70%;
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+ word-wrap: break-word;
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+ }
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+ .bot-bubble {
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+ background-color: #363636; /* Slightly lighter background color */
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+ align-self: flex-start;
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+ border-radius: 10px;
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+ padding: 8px;
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+ margin: 5px;
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+ max-width: 70%;
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+ word-wrap: break-word;
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+ }
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+ </style>
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+ """
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+ , unsafe_allow_html=True)
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+
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+ conversation = st.session_state.get("conversation", [])
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+
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+ query = st.text_input("Ask your question here:", key="user_input")
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+ if st.button("Get Answer"):
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+ if query:
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+ with st.spinner("Processing your question..."): # Display the processing message
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+ conversation.append({"role": "user", "message": query})
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+ # Call your QA function
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+ answer = qa_bot(query)
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+ conversation.append({"role": "bot", "message": answer})
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+ st.session_state.conversation = conversation
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+ else:
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+ st.warning("Please input a question.")
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+
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+ chat_container = st.empty()
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+ chat_bubbles = ''.join([f'<div class="{c["role"]}-bubble">{c["message"]}</div>' for c in conversation])
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+ chat_container.markdown(f'<div class="chat-container">{chat_bubbles}</div>', unsafe_allow_html=True)
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+
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+ if __name__ == "__main__":
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+ main()