Update app.py

app.py

@@ -1,63 +1,137 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("bartowski/aya-23-35B-GGUF")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+import os
+from typing import List, Tuple
+from langchain_community.llms import GPT4All
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+import bs4
+import textwrap
+from langchain.chains import create_retrieval_chain
+#from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_community.vectorstores import FAISS
+#from langchain_community.document_loaders import WebBaseLoader
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.embeddings import LlamaCppEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+from langchain_core.callbacks import BaseCallbackHandler
+from langchain_community.document_loaders import TextLoader
+
+local_path = "/content/drive/MyDrive/Model/aya-23-8B-Q3_K_S.gguf" # "/content/drive/MyDrive/Dorna-Llama3-8B-Instruct.Q5_0.gguf" # 
+model_path = "/content/drive/MyDrive/Model/labse.Q3_K_S.gguf" # "/content/drive/MyDrive/labse.Q6_K.gguf" # 
+text_path = "/content/drive/MyDrive/gpt4all/docs/Books/chmn.txt"
+index_path = "/content/drive/MyDrive/gpt4all/index_CHEHEL_MAJLESE_NOOR"
+
+def initialize_embeddings() -> LlamaCppEmbeddings:
+    return LlamaCppEmbeddings(model_path=model_path)
+
+def load_documents() -> List:
+    loader = TextLoader(text_path)
+    return loader.load()
+
+def split_chunks(sources: List) -> List:
+    chunks = []
+    splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=32)
+    for chunk in splitter.split_documents(sources):
+        chunks.append(chunk)
+    return chunks
+
+def generate_index(chunks: List, embeddings: LlamaCppEmbeddings) -> FAISS:
+    texts = [doc.page_content for doc in chunks]
+    metadatas = [doc.metadata for doc in chunks]
+    return FAISS.from_texts(texts, embeddings, metadatas=metadatas)
+
+class MyCustomHandler(BaseCallbackHandler):
+    def on_llm_new_token(self, token: str, **kwargs) -> None:             
+        print(token),
+
+llm = GPT4All( model=local_path, n_threads=150, streaming=True,verbose=False)#,device='cuda:Tesla T4') #
+# callbacks=[MyCustomHandler()],
+# # 1. Load, chunk and index the contents of the blog to create a retriever.
+# loader = WebBaseLoader(
+#     web_paths=("https://lilianweng.github.io/posts/2023-06-23-agent/",),
+#     bs_kwargs=dict(
+#         parse_only=bs4.SoupStrainer(
+#             class_=("post-content", "post-title", "post-header")
+#         )
+#     ),
+# )
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+#docs = loader.load()
+
+#text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+#splits = text_splitter.split_documents(docs)
+#vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
+#retriever = vectorstore.as_retriever() ########## attention
+
+embeddings = initialize_embeddings()
+rebuilIndex = input('Rebuild Index (y/n)?')
+if rebuilIndex=='y':
+  #start = time.time()
+  sources = load_documents()
+  chunks = split_chunks(sources)
+  vectorstore = generate_index(chunks, embeddings)
+  vectorstore.save_local(index_path)
+  #end = time.time()
+  #elapsed = end - start
+  #print('Elapsed time to build index: ' + str(elapsed))
+
+index = FAISS.load_local(index_path, embeddings,allow_dangerous_deserialization=True)
+
+retriver = index.as_retriever()
+
+# 2. Incorporate the retriever into a question-answering chain.
+system_prompt = (    
+    """You are an assistant for question-answering tasks. "
+    "Only use the {context} to answer: "   
+    "لطفاً فقط به زبان فارسی صحبت کن و تمام پاسخ ها را به زبان فارسی بنویس "    
+    "لطفا پاسخ هایت طولانی باشد "   
+     "اگر پاسخ سوال را نیافتی بگو نمیدانم"
+    "\n\n"""
+    
+)
+
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
 )
 
+##question_answer_chain = create_stuff_documents_chain(llm, prompt)
+##rag_chain = create_retrieval_chain(retriver  , question_answer_chain) # retriever
+
+#result = rag_chain.invoke({"input": "What is Task Decomposition?"})
+
+# second edit
+
+rag_chain_from_docs = (
+    {
+        "input": lambda x: x["input"],  # input query
+        "context": lambda x: format_docs(x["context"]),  # context
+    }
+    | prompt  # format query and context into prompt
+    | llm  # generate response
+    | StrOutputParser()  # coerce to string
+)
+
+# Pass input query to retriever
+retrieve_docs = (lambda x: x["input"]) | retriver
+
+# Below, we chain `.assign` calls. This takes a dict and successively
+# adds keys-- "context" and "answer"-- where the value for each key
+# is determined by a Runnable. The Runnable operates on all existing
+# keys in the dict.
+chain = RunnablePassthrough.assign(context=retrieve_docs).assign(
+    answer=rag_chain_from_docs
+)
+chat_history = []
+while True:
+    query = input("پرسش تان را بپرسید. حقیر در خدمتم: ")
+    
+    if query.lower() == 'exit':
+        break
 
-if __name__ == "__main__":
-    demo.launch()
+    response = chain.invoke({"input": query})
+    print(textwrap.fill(response['answer'],80))