Create app.py

app.py

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+from langchain_core.prompts import PromptTemplate
+import os 
+from langchain_community.embeddings import HuggingFaceBgeEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain_community.llms.ctransformers import CTransformers
+from langchain.chains.retrieval_qa.base import RetrievalQA
+import streamlit as st
+
+DB_FAISS_PATH = 'vectorstores/'
+
+custom_prompt_template = '''use the following pieces of information to answer the user's questions.
+If you don't know the answer, please just say that don't know the answer, don't try to make up an answer.
+Context : {context}
+Question : {question}
+only return the helpful answer below and nothing else.
+'''
+
+# custom_prompt_template = '''
+# <|im_start|>system
+# use the following pieces of information to answer the user's questions.
+# If you don't know the answer, please just say that don't know the answer, don't try to make up an answer.
+# Context : {context}
+# Question : {question}
+# only return the helpful answer below and nothing else.
+# '''
+
+
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for vector stores
+    """
+    prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+    return prompt
+
+def load_llm():
+    llm = CTransformers(
+        model='epfl-llm/meditron-7b',
+        model_type='llma',
+        max_new_token=512,
+        temperature=0.5
+    )
+    return llm
+
+def load_embeddings():
+    embeddings = HuggingFaceBgeEmbeddings(model_name='NeuML/pubmedbert-base-embeddings',
+                                          model_kwargs={'device': 'cpu'})
+    return embeddings
+
+def load_faiss_index(embeddings):
+    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
+    return db
+
+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 qa_bot():
+    embeddings = load_embeddings()
+    db = load_faiss_index(embeddings)
+    llm = load_llm()
+    qa_prompt = set_custom_prompt()
+    qa = retrieval_qa_chain(llm, qa_prompt, db)
+    return qa
+
+def final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'query': query})
+    return response
+
+import streamlit as st
+
+# Initialize the bot
+bot = qa_bot()
+
+# Streamlit webpage title
+st.title('Medical Chatbot')
+
+# User input
+user_query = st.text_input("Please enter your question:")
+
+# Button to get answer
+if st.button('Get Answer'):
+    if user_query:
+        # Call the function from your chatbot script
+        response = final_result(user_query)
+        if response:
+            # Displaying the response
+            st.write("### Answer")
+            st.write(response['result'])
+
+            # Displaying source document details if available
+            if 'source_documents' in response:
+                st.write("### Source Document Information")
+                for doc in response['source_documents']:
+                    # Retrieve and format page content by replacing '\n' with new line
+                    formatted_content = doc.page_content.replace("\\n", "\n")
+                    st.write("#### Document Content")
+                    st.text_area(label="Page Content", value=formatted_content, height=300)
+
+                    # Retrieve source and page from metadata
+                    source = doc.metadata.get('source', 'Unknown')
+                    page = doc.metadata.get('page', 'Unknown')
+                    st.write(f"Source: {source}")
+                    st.write(f"Page Number: {page}")
+                    
+        else:
+            st.write("Sorry, I couldn't find an answer to your question.")
+    else:
+        st.write("Please enter a question to get an answer.")