Upload app.py

app.py

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+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 ConversationalRetrievalChain
+
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+#Loading the model
+def load_llm():
+    # Load the locally downloaded model here
+    llm = CTransformers(
+        model = "llama-2-7b-chat.ggmlv3.q8_0.bin",
+        model_type="llama",
+        max_new_tokens = 256,
+        temperature = 0.2
+    )
+    return llm
+
+st.title("🦙Llama2🦜CSV🦙")
+st.markdown("<h3 style='color: black;'>Harness the power of LLama2 with Langchain.</h3>", unsafe_allow_html=True)
+st.markdown("<h4 style='color: black;'>Developed by <a href='https://github.com/rohan-shaw'>Rohan Shaw</a> with ❤️</h4>", unsafe_allow_html=True)
+uploaded_file = st.sidebar.file_uploader("CSV file here", type="csv")
+
+if uploaded_file :
+    with tempfile.NamedTemporaryFile(delete=False) as t:
+        t.write(uploaded_file.getvalue())
+        temp_path = t.name
+
+    loader = CSVLoader(file_path=temp_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'})
+
+    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())
+
+    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'] = ["Bhai, " + uploaded_file.name + " is file ke bare mein kuch bhi puch le aankh 👀 band karke answer dunga 🤔"]
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Aur, bol kya hal chal ! 🖖"]
+        
+    #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="Apne CSV file ke data ke bare me yaha pe puch (:", 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="human")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="pixel-art-neutral")