Create app.py

app.py

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+#from langchain import PromptTemplate
+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 import RetrievalQA
+from langchain.chains.retrieval_qa.base import RetrievalQA
+
+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 uo 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 = 'TheBloke/Llama-2-7B-Chat-GGML',
+        #model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML"),
+        model_type = 'llama',
+        max_new_token = 512,
+        temperature = 0.5
+    )
+    return llm
+
+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 = HuggingFaceBgeEmbeddings(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 final_result(query):
+    qa_result = qa_bot()
+    response = qa_result({'query' : query})
+
+    return response
+
+
+import streamlit as st
+
+# Initialize the bot
+bot = qa_bot()
+
+# def process_query(query):
+#     # Here you would include the logic to process the query and return a response
+#     response, sources = bot.answer_query(query)  # Modify this according to your bot implementation
+#     if sources:
+#         response += f"\nSources: {', '.join(sources)}"
+#     else:
+#         response += "\nNo Sources Found"
+#     return response
+def process_query(query):
+    # Here you would include the logic to process the query and return a response
+    response = bot({'query': query})
+    response_text = response['result']
+    sources = response.get('source_documents', [])
+    if sources:
+        source_list = ', '.join([source.metadata['source'] for source in sources])
+        response_text += f"\nSources: {source_list}"
+        st.write(response_text)
+    else:
+        response_text += "\nNo Sources Found"
+        st.write(response_text)
+    return response_text
+
+
+# Setting up the Streamlit app
+st.title('Medical Chatbot')
+
+user_input = st.text_input("Hi, welcome to the medical Bot. What is your query?")
+
+if user_input:
+    output = process_query(user_input)
+    st.text_area("Response", output, height=300)