app.py

import os
import re
import json
import gradio as gr
from langchain import HuggingFaceHub
from langchain.output_parsers import PydanticOutputParser
from langchain import PromptTemplate
from pydantic import BaseModel, Field
from transformers import pipeline
from typing import List
import openai



openai.api_key = os.environ['OPENAI_API_TOKEN']

HUGGINGFACEHUB_API_TOKEN=os.environ["HUGGINGFACEHUB_API_TOKEN"] 


classifier = pipeline("sentiment-analysis")
repo_id = "tiiuae/falcon-7b-instruct"
llm = HuggingFaceHub(huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, 
                     repo_id=repo_id, 
                     model_kwargs={"temperature":0.1, "max_new_tokens":300})




class Sentiment(BaseModel):
    label: str = Field(description="Is a the above rview sentiment 'Good', or 'Bad' ?")




def sentence_builder(Model,Text):
    if Model=="Sentiment analysis pipeline":
        good_label,bad_label=pipeline_sentiment(Text)
    if Model=="Falcon-7b-instruct":
        good_label,bad_label=falcon_sentiment(Text)
    if Model=="GPT-4 Function call":
        good_label,bad_label=gpt4_sentiment(Text)
        
    print({"Good": good_label, "Bad": bad_label})
    return {"Good": good_label, "Bad": bad_label}


demo = gr.Interface(
    sentence_builder,
    [
     
        gr.Dropdown(
            ["Sentiment analysis pipeline","Falcon-7b-instruct","GPT-4 Function call"], label="Model", info="Wich model to use"
        ),
        gr.Textbox(
            label="Text",
            info="Review text",
            lines=2,
            value="I'm not sure about the origin of this product, it seems suspicious.",
        ),
        
    ],
   "label",
    examples=[
        
        ["Sentiment analysis pipeline","The product broke ! Great ..."],
        ["Sentiment analysis pipeline","Not sure if I like it or not."],
        ["Sentiment analysis pipeline","This product is just a toy."],
        ["Sentiment analysis pipeline","Bought a TV, received an Ipad..."],
        ["Sentiment analysis pipeline","Could have found the same on wish.com ."],
        ["Sentiment analysis pipeline","They did a wonderfull job at ripping us."],
        ["Sentiment analysis pipeline","Is it dropshipping ?"],
    ]
)

def pipeline_sentiment(text):
    out = classifier(text)
    print(out)
    if out[0]['label'] == "NEGATIVE":
        bad_label = out[0]['score']
        good_label = 1 - bad_label
    elif out[0]['label'] == "POSITIVE":
        good_label = out[0]['score']
        bad_label = 1 - good_label
    print(good_label, bad_label)
    return good_label, bad_label

def falcon_sentiment(text):
  
    parser = PydanticOutputParser(pydantic_object=Sentiment)

    prompt = PromptTemplate(
        template="Classify the following  review as Good or Bad : .\n{format_instructions}\n{query}\n",
        input_variables=["query"],
        partial_variables={"format_instructions": parser.get_format_instructions()},
    )

    _input = prompt.format_prompt(query=text)

    output = llm(_input.to_string())
    print('Sentiment :', output)
    try:
        parsed_output=parser.parse(output)
        print("parsed_output",parsed_output)
    except:
         
        pattern = r'\b(Good|Bad)\b'
        match = re.search(pattern, output)
        print(match)
        print(match.group(0))
        parsed_output=match.group(0)
    return parsed_output=="Good",parsed_output=='Bad'

def Find_sentiment(sentence):
   
    system_msg = {"role": "system", "content": f'You an AI  that help me label sentences based on multiples features'}
    
     # Initialize messages array
    messages = [system_msg]
    message=f"Sentence: {sentence}, based on the sentence, fin the best sentiment to discribe this sentence."

    messages.append({"role": "user", "content": message})
    try:
        response = openai.ChatCompletion.create(
            model="gpt-4-0613",
            messages=messages,
            functions=[
    {
        "name": "set_sentiment",
        "description": "Set the sentiment of the sentence.",
        "parameters": {
            "type": "object",
            "properties": {
                "sentiment": {
                    "type": "string",
                    "description": "A Sentiment between 'Good' or 'Bad'",
                },
              
            },
            "required": ["sentiment"],
        },
    }
        ],
        function_call={"name": "set_sentiment"},
)
        assistant_msg = response['choices'][0]['message']
        response_options = assistant_msg.to_dict()['function_call']['arguments']
        options = json.loads(response_options)

        return options["sentiment"]
    except openai.error.OpenAIError as e:
        print("Some error happened here.",e)
        return

def gpt4_sentiment(text):
    out=Find_sentiment(text)
    return out=="Good",out=='Bad'
if __name__ == "__main__":
    
    demo.launch()