Create app.py

app.py

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+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+# Download the vader_lexicon resource
+nltk.download('vader_lexicon')
+
+text = "I absolutely loved this movie! The acting was superb, the plot was engaging, and the cinematography was stunning. I would highly recommend it to anyone looking for a great film to watch.."
+analyzer = SentimentIntensityAnalyzer()
+scores = analyzer.polarity_scores(text)
+if scores['compound'] >= 0.05:
+    print("Positive Sentiment")
+elif scores['compound'] <= -0.05:
+    print("Negative Sentiment")
+else:
+    print("Neutral Sentiment")
+
+
+import gradio as gr
+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+nltk.download('vader_lexicon')
+
+analyzer = SentimentIntensityAnalyzer()
+
+def analyze_sentiment(text):
+    scores = analyzer.polarity_scores(text)
+    if scores['compound'] >= 0.5:
+        sentiment = "Very Positive 😃"
+    elif scores['compound'] > 0 and scores['compound'] < 0.5:
+        sentiment = "Positive 🙂"
+    elif scores['compound'] == 0:
+        sentiment = "Neutral 😐"
+    elif scores['compound'] > -0.5 and scores['compound'] < 0:
+        sentiment = "Negative 🙁"
+    elif scores['compound'] <= -0.5:
+        sentiment = "Very Negative 😠"
+    elif "racist" in text.lower():
+        sentiment = "Racist 🤬"
+    elif "annoying" in text.lower():
+        sentiment = "Annoying 😒"
+    elif "boring" in text.lower():
+        sentiment = "Boring 😴"
+    else:
+        sentiment = "Unknown 😕"
+    return sentiment, text
+
+iface = gr.Interface(fn=analyze_sentiment, 
+                     inputs=gr.inputs.Textbox(label="Enter Text Here"), 
+                     outputs=[gr.outputs.Textbox(label="Sentiment"), 
+                              gr.outputs.Textbox(label="Input Text")],
+                     title="Sentiment Analysis", 
+                     description="Enter a sentence and get the sentiment analysis result.")
+
+iface.launch() 
+