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imalexianne
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Parent(s):
b3fe5ba
Update app.py
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app.py
CHANGED
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import os
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import gradio as gr
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from transformers import AutoTokenizer, AutoModel
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from scipy.special import softmax
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from huggingface_hub import login
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load_dotenv()
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login(os.getenv("access_token"))
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# Requirements
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model_path = "imalexianne/distilbert-base-uncased"
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# tokenizer = AutoTokenizer.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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#
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def preprocess(text):
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new_text = []
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for
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new_text.append(
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return " ".join(new_text)
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# Function to process the input and return prediction
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def sentiment_analysis(text):
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text = preprocess(text)
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@@ -41,14 +51,15 @@ def sentiment_analysis(text):
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return scores
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app
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import os
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import gradio as gr
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import numpy as np
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from transformers import AutoTokenizer, AutoModel
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from scipy.special import softmax
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import gradio as gr
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import numpy as np
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import pandas as pd
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import pickle
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import transformers
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from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification,TFAutoModelForSequenceClassification, pipeline
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from scipy.special import softmax
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from dotenv import load_dotenv, dotenv_values
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from huggingface_hub import login
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load_dotenv()
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login(os.getenv("access_token"))
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# Requirements
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# huggingface_token = "" # Replace with your actual token
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model_path = "imalexianne/distilbert-base-uncased"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
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# tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", revision="main")
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config = AutoConfig.from_pretrained(model_path)
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model = AutoModelForSequenceClassification.from_pretrained(model_path)
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# Preprocessessing function
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def preprocess(text):
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new_text = []
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for t in text.split(" "):
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t = "@user" if t.startswith("@") and len(t) > 1 else t
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t = "http" if t.startswith("http") else t
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new_text.append(t)
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return " ".join(new_text)
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# ---- Function to process the input and return prediction
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def sentiment_analysis(text):
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text = preprocess(text)
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return scores
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# ---- Gradio app interface
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app = gr.Interface(fn = sentiment_analysis,
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inputs = gr.Textbox("Write your text here..."),
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outputs = "label",
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title = "Sentiment Analysis of Tweets on COVID-19 Vaccines",
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description = "Sentiment Analysis of text based on tweets about COVID-19 Vaccines using a fine-tuned 'distilbert-base-uncased' model",
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examples = [["Covid vaccination has no positive impact"]]
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)
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app.launch()
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