Delete app.py

app.py

@@ -1,81 +0,0 @@
-import streamlit as st
-import os
-from langchain.document_loaders.csv_loader import CSVLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.llms import CTransformers
-from langchain.chains import ConversationalRetrievalChain
-
-def add_vertical_space(spaces=1):
-    for _ in range(spaces):
-        st.sidebar.markdown("---")
-
-def main():
-    st.set_page_config(page_title="Llama-2-GGML CSV Chatbot", layout="wide")
-    st.title("Llama-2-GGML CSV Chatbot")
-
-    st.sidebar.title("About")
-    st.sidebar.markdown('''
-        The Llama-2-GGML CSV Chatbot uses the **Llama-2-7B-Chat-GGML** model.
-        
-        ### 🔄Bot evolving, stay tuned!
-        
-        ## Useful Links 🔗
-        
-        - **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main) 📚
-        - **GitHub:** [ThisIs-Developer/Llama-2-GGML-CSV-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot) 💬
-    ''')
-
-    DB_FAISS_PATH = "vectorstore/db_faiss"
-    TEMP_DIR = "temp"
-
-    if not os.path.exists(TEMP_DIR):
-        os.makedirs(TEMP_DIR)
-
-    uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'], help="Upload a CSV file")
-
-    add_vertical_space(1)
-    st.sidebar.markdown('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')
-
-    if uploaded_file is not None:
-        file_path = os.path.join(TEMP_DIR, uploaded_file.name)
-        with open(file_path, "wb") as f:
-            f.write(uploaded_file.getvalue())
-
-        st.write(f"Uploaded file: {uploaded_file.name}")
-        st.write("Processing CSV file...")
-
-        loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','})
-        data = loader.load()
-
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-        text_chunks = text_splitter.split_documents(data)
-
-        st.write(f"Total text chunks: {len(text_chunks)}")
-
-        embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
-        docsearch = FAISS.from_documents(text_chunks, embeddings)
-        docsearch.save_local(DB_FAISS_PATH)
-
-        llm = CTransformers(model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
-                            model_type="llama",
-                            max_new_tokens=512,
-                            temperature=0.1)
-
-        qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())
-
-        st.write("### Enter your query:")
-        query = st.text_input("Input Prompt:")
-        if query:
-            with st.spinner("Processing your question..."):
-                chat_history = []
-                result = qa({"question": query, "chat_history": chat_history})
-                st.write("---")
-                st.write("### Response:")
-                st.write(f"> {result['answer']}")
-
-        os.remove(file_path)
-
-if __name__ == "__main__":
-    main()