Create app.py

app.py

@@ -0,0 +1,81 @@
+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()