|
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 the above sentiment 'Good', or 'Bad' ?") |
|
|
|
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'} |
|
|
|
|
|
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' |
|
|
|
|
|
|
|
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(Model, 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."], |
|
|
|
] |
|
) |
|
if __name__ == "__main__": |
|
|
|
demo.launch() |
|
|
