app file

tuto_chatbot_csv.py

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+#pip install streamlit langchain openai faiss-cpu tiktoken
+
+import streamlit as st
+from streamlit_chat import message
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.chat_models import ChatOpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.vectorstores import FAISS
+import tempfile
+from htmlTemplates import css, bot_template, user_template
+from dotenv import load_dotenv
+import os
+
+load_dotenv('.env')
+
+# Access the API key
+api_key = os.getenv('API_KEY')
+#user_api_key = "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"
+#st.sidebar.text_input(
+    #label="#### Your OpenAI API key 👇",
+    #placeholder="Paste your openAI API key, sk-",
+    #type="password")
+#logo=
+uploaded_file = st.sidebar.file_uploader("  UPLOAD YOUR FILE", type="csv")
+
+if uploaded_file :   
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
+
+    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8")
+    data = loader.load()
+
+    embeddings = OpenAIEmbeddings(openai_api_key=api_key)
+    vectors = FAISS.from_documents(data, embeddings)
+
+    chain = ConversationalRetrievalChain.from_llm(llm=ChatOpenAI(temperature=0.0, model_name='gpt-3.5-turbo', openai_api_key=api_key),
+    retriever=vectors.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()
+
+    # Apply CSS styles
+    st.write(css, unsafe_allow_html=True)
+
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            
+            user_input = st.text_input("Query:", placeholder="Talk about 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")
+                
+#streamlit run tuto_chatbot_csv.py