Duplicate from zac/llama-cpp-python

Co-authored-by: isAAC <zac@users.noreply.huggingface.co>

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README.md

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+---
+title: (LLM on CPU)/ DOC Readers
+emoji: 👾
+colorFrom: pink
+colorTo: red
+sdk: gradio
+sdk_version: 3.37.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+duplicated_from: zac/llama-cpp-python
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import copy
+import time
+import ctypes #to run on C api directly 
+import llama_cpp
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download #load from huggingfaces 
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+
+llm = Llama(model_path= hf_hub_download(repo_id="TheBloke/Dolphin-Llama2-7B-GGML", filename="dolphin-llama2-7b.ggmlv3.q4_1.bin"), n_ctx=2048) #download model from hf/ n_ctx=2048 for high ccontext length
+
+history = []
+
+pre_prompt = " The user and the AI are having a conversation : <|endoftext|> \n "
+
+def get_pdf_text(pdfs):
+  text=""
+  for pdf in pdfs:
+    pdf_reader = PdfReader(pdf)
+    for page in pdf_reader.pages:
+      text+= page.extract_text()
+  return text
+
+def get_text_chunks(text):
+  text_splitter = CharacterTextSplitter(separator="\n",
+  chunk_size=1000, chunk_overlap = 200, length_function=len)
+  chunks = text_splitter.split_text(text)
+  return chunks
+
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings()
+#     embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+def generate_text(input_text, history):
+    print("history ",history)
+    print("input ", input_text)
+    temp =""
+    if history == []:
+        input_text_with_history = f"SYSTEM:{pre_prompt}"+ "\n" + f"USER: {input_text} " + "\n" +" ASSISTANT:"
+    else:
+        input_text_with_history = f"{history[-1][1]}"+ "\n"
+        input_text_with_history += f"USER: {input_text}" + "\n" +" ASSISTANT:"
+    print("new input", input_text_with_history)
+    output = llm(input_text_with_history, max_tokens=1024, stop=["<|prompter|>", "<|endoftext|>", "<|endoftext|> \n","ASSISTANT:","USER:","SYSTEM:"], stream=True)
+    for out in output:
+     stream = copy.deepcopy(out)
+     print(stream["choices"][0]["text"])
+     temp += stream["choices"][0]["text"]
+     yield temp
+
+
+    history =["init",input_text_with_history]
+        
+
+
+demo = gr.ChatInterface(generate_text,
+    title="LLM on CPU",
+    description="Running LLM with https://github.com/abetlen/llama-cpp-python. btw the text streaming thing was the hardest thing to impliment",
+    examples=["Hello", "Am I cool?", "Are tomatoes vegetables?"],
+    cache_examples=True,
+    retry_btn=None,
+    undo_btn="Delete Previous",
+    clear_btn="Clear",)
+demo.queue(concurrency_count=1, max_size=5)
+demo.launch()
+
+
+
+

requirements.txt

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+llama-cpp-python
+huggingface_hub
+
+langchain==0.0.222
+PyPDF2==3.0.1
+python-dotenv==1.0.0
+streamlit==1.24.0
+streamlit_chat==0.1.1
+openai==0.27.8
+tiktoken==0.4.0
+faiss-cpu