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README.md

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-# NLP-Sentiment-Analysis
-This is an analysis that took peoples sentiments and classified into 3 categories and modeled with a pretrained model
+---
+title: Sentiment Analysis NLP
+emoji: 🔥
+colorFrom: red
+colorTo: indigo
+sdk: gradio
+sdk_version: 3.44.3
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

src/app.py

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+# -*- coding: utf-8 -*- 
+# """gradio_app.ipynb
+
+# Automatically generated by Colaboratory.
+
+# Original file is located at
+#     https://colab.research.google.com/drive/1u8oKw0KTptVWpY-cKFL87N2IDDrM4lTc
+# """
+##
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import pickle
+from scipy.special import softmax
+from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
+
+
+# Requirements
+model_path = "QuophyDzifa/Sentiment-Analysis-Model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+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 sent_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 = {0: 'NEGATIVE', 1: 'NEUTRAL', 2: 'POSITIVE'}
+    scores = {labels[i]: float(s) for i, s in enumerate(scores_)}
+    return scores
+
+
+demo = gr.Interface(
+    fn=sent_analysis,
+    inputs=gr.Textbox(placeholder="Share your thoughts on COVID vaccines..."),
+    outputs="label",
+    interpretation="default",
+    examples=[
+        ["I feel confident about covid vaccines"],
+        ["I do not like the covid vaccine"],
+        ["I like the covid vaccines"],
+        ["The covid vaccines are effective"]
+    ],
+    title="COVID Vaccine Sentiment Analysis",
+    description="An AI model that predicts sentiment about COVID vaccines, providing labels and probabilities for 'NEGATIVE', 'NEUTRAL', and 'POSITIVE' sentiments.",
+    theme="default",
+    live=True
+)
+
+if __name__ == "__main__":
+    demo.launch("0.0.0.0:7860")

src/requirements.txt

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+gradio==3.28.0
+transformers==4.28.1
+numpy==1.23.5
+scikit-learn==1.3.0
+torch
+scipy==1.10.1
+black
+
+
+
+
+
+
+
+# gradio
+# pandas
+# transformers
+# numpy
+# scikit-learn
+# gdown
+# torch
+# scipy
+
+# tabulate
+# seaborn
+# datasets>=2.14.5
+# transformers>=4.11.2
+# huggingface-hub>=0.0.17s
+# wordcloud>=1.8.1
+# matplotlib>=3.4.3
+# google-colab>=1.0.0
+# datasets
+# transformers
+# huggingface-hub
+# wordcloud
+# matplotlib
+# google-colab