app.py

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 gradio as gr

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 load_llm():
    llm = CTransformers(
        model="llama-2-7b-chat.ggmlv3.q8_0.bin",
        model_type="llama",
        max_new_tokens=512,
        temperature=0.5
    )
    return llm

def retrieval_QA_chain(llm, prompt, db):
    qachain = 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 qachain

def qa_bot():
    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-miniLM-L6-V2', model_kwargs={'device': 'cpu'})
    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
    llm = load_llm()
    qa_prompt = set_custom_prompt()
    qa = retrieval_QA_chain(llm, qa_prompt, db)
    return qa

def chatbot_response(query):
    try:
        qa = qa_bot()
        response = qa({'query': query})
        answer = response["result"]
        sources = response["source_documents"]
        if sources:
            answer += f"\nSources:" + str(sources)
        else:
            answer += "\nNo sources found"
        return answer
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Create a Gradio interface with updated API
iface = gr.Interface(
    fn=chatbot_response,
    inputs=gr.Textbox(lines=2, placeholder="Enter your question..."),
    outputs="text",
    title="Medical Chatbot",
    description="Ask a medical question and get answers based on the provided context."
)

# Launch the Gradio app
iface.launch()