Create app.py

app.py

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+from langchain.chains import ConversationalRetrievalChain
+from langchain.chains.question_answering import load_qa_chain
+from langchain.chains import RetrievalQA
+from langchain.memory import ConversationBufferMemory
+from langchain.memory import ConversationTokenBufferMemory
+from langchain.llms import HuggingFacePipeline
+# from langchain import PromptTemplate
+from langchain.prompts import PromptTemplate
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.document_loaders import (
+    CSVLoader,
+    DirectoryLoader,
+    GitLoader,
+    NotebookLoader,
+    OnlinePDFLoader,
+    PythonLoader,
+    TextLoader,
+    UnstructuredFileLoader,
+    UnstructuredHTMLLoader,
+    UnstructuredPDFLoader,
+    UnstructuredWordDocumentLoader,
+    WebBaseLoader,
+    PyPDFLoader,
+    UnstructuredMarkdownLoader,
+    UnstructuredEPubLoader,
+    UnstructuredHTMLLoader,
+    UnstructuredPowerPointLoader,
+    UnstructuredODTLoader,
+    NotebookLoader,
+    UnstructuredFileLoader
+)
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    StoppingCriteria,
+    StoppingCriteriaList,
+    pipeline,
+    GenerationConfig,
+    TextStreamer,
+    pipeline
+)
+from langchain.llms import HuggingFaceHub
+import torch
+from transformers import BitsAndBytesConfig
+import os
+from langchain.llms import CTransformers
+import streamlit as st
+from langchain.document_loaders.base import BaseLoader
+from langchain.schema import Document
+import gradio as gr
+import tempfile
+import timeit
+
+FILE_LOADER_MAPPING = {
+    "csv": (CSVLoader, {"encoding": "utf-8"}),
+    "doc": (UnstructuredWordDocumentLoader, {}),
+    "docx": (UnstructuredWordDocumentLoader, {}),
+    "epub": (UnstructuredEPubLoader, {}),
+    "html": (UnstructuredHTMLLoader, {}),
+    "md": (UnstructuredMarkdownLoader, {}),
+    "odt": (UnstructuredODTLoader, {}),
+    "pdf": (PyPDFLoader, {}),
+    "ppt": (UnstructuredPowerPointLoader, {}),
+    "pptx": (UnstructuredPowerPointLoader, {}),
+    "txt": (TextLoader, {"encoding": "utf8"}),
+    "ipynb": (NotebookLoader, {}),
+    "py": (PythonLoader, {}),
+    # Add more mappings for other file extensions and loaders as needed
+}
+
+def load_model():
+    config = {'max_new_tokens': 1024,
+              'repetition_penalty': 1.1,
+              'temperature': 0.1,
+              'top_k': 50,
+              'top_p': 0.9,
+              'stream': True,
+              'threads': int(os.cpu_count() / 2)
+            }
+    
+    llm = CTransformers(
+        model = "TheBloke/zephyr-7B-beta-GGUF",
+        model_file = "zephyr-7b-beta.Q4_0.gguf",
+        callbacks=[StreamingStdOutCallbackHandler()],
+        lib="avx2", #for CPU use
+        **config
+        # model_type=model_type,
+        # max_new_tokens=max_new_tokens,  # type: ignore
+        # temperature=temperature,  # type: ignore
+    )
+    return llm