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README.md

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 ---
-title: Reviews-Sentiment-Analysis App
-emoji: 💻
-colorFrom: green
-colorTo: indigo
+title: Reviews Sentiment Analysis App
+emoji: 🏃
+colorFrom: red
+colorTo: pink
 sdk: gradio
 sdk_version: 3.16.2
 app_file: app.py
 pinned: false
-license: openrail
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+# Import the required Libraries
+import gradio as gr
+import numpy as np
+import pandas as pd
+import pickle
+import transformers 
+from transformers import AutoTokenizer, AutoConfig,AutoModelForSequenceClassification,TFAutoModelForSequenceClassification
+from scipy.special import softmax
+# Requirements
+model_path = "Kaludi/Reviews-Sentiment-Analysis"
+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)
+
+# ---- Function to process the input and return prediction
+def sentiment_analysis(text):
+    text = preprocess(text)
+
+    encoded_input = tokenizer(text, return_tensors = "pt") # for PyTorch-based models
+    output = model(**encoded_input)
+    scores_ = output[0][0].detach().numpy()
+    scores_ = softmax(scores_)
+    
+    # Format output dict of scores
+    labels = ["Negative", "Positive"]
+    scores = {l:float(s) for (l,s) in zip(labels, scores_) }
+    
+    return scores
+
+
+# ---- Gradio app interface
+app = gr.Interface(fn = sentiment_analysis,
+                   inputs = gr.Textbox("Write your text or review here..."),
+                   outputs = "label",
+                   title = "Sentiment Analysis of Customer Reviews",
+                   description  = "A tool that analyzes the overall sentiment of customer reviews for a specific product or servicem, wheather it's positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model 'Reviews-Sentiment-Analysis' trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.",
+                   interpretation = "default",
+                   examples = [["I was extremely disappointed with this product. The quality was terrible and it broke after only a few days of use. Customer service was unhelpful and unresponsive. I would not recommend this product to anyone.", "This product was great! My family and I found it very useful."]]
+                   )
+
+app.launch()
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requirements.txt

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+fastai
+gensim
+gradio
+ipywidgets
+jupyter
+nltk
+notebook
+pandas
+plotly
+pytest
+scikit-learn
+seaborn
+setuptools
+simpletransformers
+spacy
+transformers
+wheel