import os
os.system('pip install torch')
os.system('pip install transformers')
os.system('pip install scipy')
os.system('pip install gradio')
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
from scipy.special import softmax
import gradio as gr

# Requirements
model_path = f"Winnie-Kay/Finetuned_BertModel_SentimentAnalysis"
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
config = AutoConfig.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Preprocess text (username and link placeholders)
def preprocess(text):
    new_text = []
    for t in text.split(" "):
        t = '@user' if t.startswith('@') and len(t) > 1 else t
        t = 'http' if t.startswith('http') else t
        new_text.append(t)
    return " ".join(new_text)


def sentiment_analysis(text):
    text = preprocess(text)

    # PyTorch-based models
    encoded_input = tokenizer(text, return_tensors='pt')
    output = model(**encoded_input)
    scores_ = output[0][0].detach().numpy()
    scores_ = softmax(scores_)
    
    # Format output dict of scores
    labels = ['Negative', 'Neutral', 'Positive']
    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
    
    return scores

demo = gr.Interface(
    fn=sentiment_analysis, 
    inputs=gr.Textbox(placeholder="Write your tweet here..."), 
    outputs="label", 
    interpretation="default",
    examples=[["This is wonderful!"]])

demo.launch()