app.py

import gradio as gr
from huggingface_hub import InferenceClient
## Import required packages

from langchain_community.llms import CTransformers
from langchain.prompts import FewShotChatMessagePromptTemplate, ChatPromptTemplate, FewShotPromptTemplate
import gradio as gr
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
# from langchain.document_loaders import WikipediaLoader
from langchain.document_loaders import JSONLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from operator import itemgetter
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.outputs import Generation
from typing import Any, List, Optional, Type, TypeVar, Union

## Defining few variables
MODEL_PATH = "TheBloke/Mistral-7B-Claude-Chat-GGUF"
MODEL_FILE = "mistral-7b-claude-chat.Q4_K_M.gguf"
MODEL_TYPE = "mistral"
MAX_NEW_TOKENS = 100
temperature = 1
top_p = 0.95
top_k = 50
repetition_penalty = 1.5

## Defining Model
llm = CTransformers(
    model = MODEL_PATH,
    model_file=MODEL_FILE,
    model_type = MODEL_TYPE,
    config = {
        "max_new_tokens":MAX_NEW_TOKENS,
        "temperature": temperature,
        "top_p": top_p,
        "top_k": top_k,
        "repetition_penalty": repetition_penalty,
        "last_n_tokens": 4,
        "stream": True,
        "gpu_layers": 1000
    }
)

# One shot inferencing
examples = [
    {
        "query": "Please classify this name: Ketan Jogadankar",
        "answer":"""{
            "name": "Ketan Jogadankar",
            "label": "person",
            "score": 0.99,
            "reason": "Ketan is a most famous first name and Jogadankar looks like a surname."
        }"""
    }
]

example_template = """
User: {query}
{answer}
"""

example_prompt = ChatPromptTemplate.from_messages(
    [("human", "{query}"),
        ("ai", "{answer}")]
)

prefix = """Act as an AI assistant that classifies names into 3 categories (person, business and other) based on the provided rules and example data.
{format_instructions}
Do not append any text to human input.
Rules:
* If the names contains the word "POD", classify it as a other.
* If the names contains the word "trust", classify it as a other.
* If the names contains the word "llc", classify it as a business.
* If the name is non-profit organization then classify it as a other.
Here are some examples:
"""

suffix = """Please classify this name: {name}
"""

few_shot_prompt_template = FewShotChatMessagePromptTemplate(
    examples = examples,
    example_prompt = example_prompt
)

prompt = ChatPromptTemplate.from_messages(
    [
        ("system",prefix),
        few_shot_prompt_template,
        ("human", suffix)
    ]
)

format_instructions = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
Here is the output schema:
```
{"properties": {"name": {"title": "Name", "description": "this is the input name passed by human", "type": "string"}, "label": {"title": "Label", "description": "this is the label predicted for input name", "type": "string"}, "score": {"title": "Score", "description": "This is confidence score for predicted label", "type": "number"}, "reason": {"title": "Reason", "description": "This is to explain why AI has predicted that label", "type": "string"}}, "required": ["name", "label", "score", "reason"]}
```
"""
# RAG
data_loader = JSONLoader(file_path="/content/sample_data/anscombe.json",
                         jq_schema='.',text_content=False)
data = data_loader.load()
data = [doc.page_content for doc in data]

splitter = CharacterTextSplitter(chunk_size=2, chunk_overlap=1)
documents = splitter.create_documents(texts=data)

docs_str = [doc.page_content for doc in documents]
sentence_emb = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

db = Chroma.from_texts(docs_str, sentence_emb, persist_directory="./temp_db")
db.persist()

retriever = db.as_retriever(
    search_type="similarity",
    search_kwargs={'k':1})

# Pydantic output validator
from pydantic import BaseModel, Field
class NameClassification(BaseModel):
    name:str = Field(description="this is the input name passed by human")
    label:str = Field(description="this is the label predicted for input name")
    score:float = Field(description="This is confidence score for predicted label")
    reason:str = Field(description="This is to explain why AI has predicted that label")

    def remove_junks(self, text):
        start_index = text.index("{")
        stop_index = text.index("}") + 1
        return text[start_index:stop_index+1]

    def parse(self, text):
        text = self.remove_junks(text)
        super().invoke(text)

class CustomParser(JsonOutputParser):

    def parse_result(self, result: List[Generation], *, partial: bool = False) -> Any:
        text = result[0].text
        text = text.strip()
        text = self.remove_junks(text)
        result = [Generation(text= text)]
        return super().parse_result(result=result,partial=partial)


    def remove_junks(self, text):
        start_index = text.index("{")
        stop_index = text.index("}") + 1
        return text[start_index:stop_index+1]

parser = CustomParser(pydantic_object=NameClassification)

chain = (
        {"context": itemgetter("name") | retriever,
         "format_instructions": itemgetter("format_instructions"),
         "name": itemgetter("name")}
        | prompt
        | llm
        | parser
)

"""
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("HuggingFaceH4/zephyr-7b-beta")

def predict(message, history, min_hist_memo = 3):
    # streamer = chain(message)
    streamer = chain.invoke({"name":message, "format_instructions":format_instructions})
    yield str(streamer)

gr.ChatInterface(predict, title="Mistral 7B").queue().launch(debug=True)

# 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)",
#         ),
#     ],
# )


# if __name__ == "__main__":
#     demo.launch()