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+# Use an official Python runtime as the base image
+FROM python:3.9-slim
+
+# Set the working directory within the container
+WORKDIR /app
+
+# Copy the requirements.txt file into the container
+COPY ./requirements.txt /app/requirements.txt
+
+# Install the Python dependencies
+RUN pip install -r /app/requirements.txt
+
+# Copy the Gradio application code into the container
+COPY ./app.py /app/app.py
+
+# Download NLTK resources
+RUN python -m nltk.downloader stopwords
+RUN python -m nltk.downloader wordnet
+
+# Expose port 7860 to access the Gradio interface
+EXPOSE 7860
+
+# Command to run the Gradio app
+CMD ["python", "app.py"]

app.py

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+# -*- coding: utf-8 -*-
+"""app.py
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1SKjRNc67_9TZPKUGhtfiYMfcpZuMh6s0
+"""
+
+# %pip install gradio transformers -q
+# %pip install nltk
+
+# Import the key libraries
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from scipy.special import softmax
+import nltk
+import re
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Download NLTK resources (if not already downloaded)
+nltk.download('stopwords')
+nltk.download('wordnet')
+
+# Load the tokenizer and model from Hugging Face
+model_path = "rasmodev/Covid-19_Sentiment_Analysis_RoBERTa_Model"
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+model = AutoModelForSequenceClassification.from_pretrained(model_path)
+
+# Preprocess text (username and link placeholders, and text preprocessing)
+def preprocess(text):
+    # Convert text to lowercase
+    text = text.lower()
+
+    # Remove special characters, numbers, and extra whitespaces
+    text = re.sub(r'[^a-zA-Z\s]', '', text)
+
+    # Remove stopwords (common words that don't carry much meaning)
+    stop_words = set(stopwords.words('english'))
+    words = text.split()  # Split text into words
+    words = [word for word in words if word not in stop_words]
+
+    # Lemmatize words to their base form
+    lemmatizer = WordNetLemmatizer()
+    words = [lemmatizer.lemmatize(word) for word in words]
+
+    # Rejoin the preprocessed words into a single string
+    processed_text = ' '.join(words)
+
+    # Process placeholders
+    new_text = []
+    for t in processed_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)
+
+# Perform sentiment analysis
+def sentiment_analysis(text):
+    text = preprocess(text)
+
+    # Tokenize input text
+    inputs = tokenizer(text, return_tensors='pt')
+
+    # Forward pass through the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    # Get predicted probabilities
+    scores_ = outputs.logits[0].detach().numpy()
+    scores_ = softmax(scores_)
+
+    # Define labels and corresponding colors
+    labels = ['Negative', 'Neutral', 'Positive']
+    colors = ['red', 'yellow', 'green']
+    font_colors = ['white', 'black', 'white']
+
+    # Find the label with the highest percentage
+    max_label = labels[scores_.argmax()]
+    max_percentage = scores_.max() * 100
+
+    # Create HTML for the label with the specified style
+    label_html = f'<div style="display: flex; justify-content: center;"><button style="text-align: center; font-size: 16px; padding: 10px; border-radius: 15px; background-color: {colors[labels.index(max_label)]}; color: {font_colors[labels.index(max_label)]};">{max_label}({max_percentage:.2f}%)</button></div>'
+
+    return label_html
+
+# Create a Gradio interface
+interface = gr.Interface(
+    fn=sentiment_analysis,
+    inputs=gr.Textbox(placeholder="Write your tweet here..."),
+    outputs=gr.HTML(),
+    title="COVID-19 Sentiment Analysis App",
+    description="This App Analyzes the sentiment of COVID-19 related tweets. Negative: Indicates a negative sentiment, Neutral: Indicates a neutral sentiment, Positive: Indicates a positive sentiment.",
+    theme="default",
+    layout="horizontal",
+    examples=[
+        ["This vaccine is terrible!"],
+        ["I don't have a strong opinion about this vaccines."],
+        ["The Vaccine is Good I have had no issues!"]
+    ]
+)
+
+# Launch the Gradio app
+interface.launch()
+

requirements.txt

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+gradio==3.44.2
+torch==2.0.1
+transformers==4.33.1
+nltk==3.8.1
+scipy==1.11.2