Update app.py

app.py

@@ -9,14 +9,62 @@ from langchain.memory import ConversationBufferMemory
 from langchain.chains import ConversationalRetrievalChain
 import sys
 
-st.title("Chat with csv using Open Source Inference point")
-
-
-DB_FAISS_PATH = "vectorstore/db_faiss"
-loader = CSVLoader(file_path="data/2019.csv", encoding="utf-8", csv_args={'delimiter': ','})
-data = loader.load()
-print(data)
-
+st.title("Chat with CSV using open source LLM Inference Point 🦙🦜")
+st.markdown("<h3 style='text-align: center; color: white;'>Built by <a href='https://github.com/AIAnytime'>AI Anytime with ❤️ </a></h3>", unsafe_allow_html=True)
+
+uploaded_file = st.sidebar.file_uploader("Upload your Data", type="csv")
+
+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
+
+    db = DB_FAISS_PATH = "vectorstore/db_faiss"
+    loader = CSVLoader(file_path="data/2019.csv", encoding="utf-8", csv_args={'delimiter': ','})
+    data = loader.load()
+    db.save_local(DB_FAISS_PATH)
+    llm = load_llm()
+
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
+
+    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 '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()
+
+    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 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")
+            
 # Split the text into Chunks
 text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
 text_chunks = text_splitter.split_documents(data)
@@ -24,7 +72,8 @@ text_chunks = text_splitter.split_documents(data)
 print(len(text_chunks))
 
 # Download Sentence Transformers Embedding From Hugging Face
-embeddings = HuggingFaceEmbeddings(model_name = 'sentence-transformers/all-MiniLM-L6-v2')
+embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                       model_kwargs={'device': 'cpu'})
 
 # COnverting the text Chunks into embeddings and saving the embeddings into FAISS Knowledge Base
 docsearch = FAISS.from_documents(text_chunks, embeddings)