sentiment analysis

SentimentAnalysis.py

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+# File: SentimentAnalysis.py
+# Project: Sentiment Analysis using Pretrained Models
+
+
+from transformers import pipeline
+
+def sentiment_analysis(text):
+    # Load pretrained model and tokenizer (DistilBERT)
+    sentiment_model = pipeline("sentiment-analysis")
+
+    # Get prediction
+    result = sentiment_model(text)[0]
+
+    # Print and return result
+    print(f"\n🧠 Input Text: {text}")
+    print(f"📊 Sentiment: {result['label']} (Confidence: {result['score']:.2f})")
+
+    return result
+
+if __name__ == "__main__":
+    print("=== Sentiment Analysis using Pretrained Models ===")
+    user_input = input("Enter a sentence or paragraph: ")
+    sentiment_analysis(user_input)

typeconversion.py

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+# Sno 3 : "Type Conversion and validation examples: Implement a Python program to type conversion and validation examples"
+
+#conversion of tuple to list
+tuple=(1,2,3,4,5)
+list1=list(tuple)
+print("conversion of tuple to list",list1)
+#conversion of int to float
+a=22
+print("conversion of int to float",float(a))
+#conversion of float to int
+b=33.0
+print("conversion of float to int",int(b))
+#int to string conversion
+c=7888
+print("int to string conversion",str(c))
+#string len to float
+s="swe"
+print("string to float",float(len(s)))