app file is updated

app.py

@@ -1,81 +1,81 @@
-import streamlit as st
-from langchain.document_loaders import PyPDFLoader, DirectoryLoader
-from langchain import PromptTemplate
+import streamlit as st 
+from streamlit_chat import message
+import tempfile
+from langchain.document_loaders.csv_loader import CSVLoader
 from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.llms import CTransformers
-from langchain.chains import RetrievalQA
-import chainlit as cl
+from langchain.chains import ConversationalRetrievalChain
 
 DB_FAISS_PATH = 'vectorstore/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 template for QA retrieval for each vectorstore
-    """
-    prompt = PromptTemplate(template=custom_prompt_template,
-                            input_variables=['context', 'question'])
-    return prompt
-
-# Retrieval QA Chain
-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
-
-# Loading the model
+#Loading the model
 def load_llm():
     # Load the locally downloaded model here
     llm = CTransformers(
-        model="llama-2-7b-chat.ggmlv3.q8_0.bin",
+        model = "llama-2-7b-chat.ggmlv3.q8_0.bin",
         model_type="llama",
-        max_new_tokens=512,
-        temperature=0.5
+        max_new_tokens = 512,
+        temperature = 0.5
     )
     return llm
 
-# QA Model Function
-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)
-    llm = load_llm()
-    qa_prompt = set_custom_prompt()
-    qa = retrieval_qa_chain(llm, qa_prompt, db)
+st.title("Chat with CSV using Llama2 🦙🦜")
+st.markdown("<h3 style='text-align: center; color: white;'> A Very Good Bot For CSV</h3>", unsafe_allow_html=True)
+
+uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")
 
-    return qa
+if uploaded_file :
+   #use tempfile because CSVLoader only accepts a file_path
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
 
-def main():
-    st.title("Medical Assistance AI ChatBot")
+    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={
+                'delimiter': ','})
+    data = loader.load()
+    #st.json(data)
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                       model_kwargs={'device': 'cpu'})
 
-    qa_result = qa_bot()
+    db = FAISS.from_documents(data, embeddings)
+    db.save_local(DB_FAISS_PATH)
+    llm = load_llm()
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
 
-    user_input = st.text_input("Enter your question:")
+    def conversational_chat(query):
+        result = chain({"question": query, "chat_history": st.session_state['history']})
+        st.session_state['history'].append((query, result["answer"]))
+        return result["answer"]
     
-    if st.button("Ask"):
-        response = qa_result({'query': user_input})
-        answer = response["result"]
-        sources = response["source_documents"]
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ["Hello ! Ask me anything about " + uploaded_file.name + " 🤗"]
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey ! 👋"]
+        
+    #container for the chat history
+    response_container = st.container()
+    #container for the user's text input
+    container = st.container()
 
-        st.write("Answer:", answer)
-        if sources:
-            st.write("Sources:", sources)
-        else:
-            st.write("No sources found")
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            
+            user_input = st.text_input("Query:", placeholder="Talk to your csv data here (:", key='input')
+            submit_button = st.form_submit_button(label='Send')
+            
+        if submit_button and user_input:
+            output = conversational_chat(user_input)
+            
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
 
-if __name__ == "__main__":
-    main()
+    if st.session_state['generated']:
+        with response_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")