added app and requirement files

app.py

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+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+
+# Function to load the pre-trained model
+def load_model(model_name):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSequenceClassification.from_pretrained(model_name)
+    sentiment_pipeline = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
+    return sentiment_pipeline
+
+# Streamlit app
+st.title("Basic Sentiment Analysis App")
+st.write("Enter a text and select a pre-trained model to get the sentiment analysis.")
+
+# Input text
+text = st.text_input("Enter your text:")
+
+# Model selection
+model_options = [
+    "distilbert-base-uncased-finetuned-sst-2-english",
+    "textattack/bert-base-uncased-SST-2",
+    "cardiffnlp/twitter-roberta-base-sentiment",
+    "nlptown/bert-base-multilingual-uncased-sentiment"
+]
+
+selected_model = st.selectbox("Choose a pre-trained model:", model_options)
+
+# Load the model and perform sentiment analysis
+if st.button("Analyze"):
+    if not text:
+        st.write("Please enter a text.")
+    else:
+        with st.spinner("Analyzing sentiment..."):
+            sentiment_pipeline = load_model(selected_model)
+            result = sentiment_pipeline(text)
+            st.write(f"Sentiment: {result[0]['label']} (confidence: {result[0]['score']:.2f})")
+else:
+    st.write("Enter a text and click 'Analyze' to perform sentiment analysis.")

requirements.txt

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+streamlit
+torch
+transformers