added app

app.py

@@ -0,0 +1,41 @@
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification,tokenzir
+from scipy.special import softmax
+
+
+# Load the fine-tuned model and tokenizer
+model_dir = "./"
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+
+# Create a Streamlit app
+st.title('Sentiment Analysis with Fine Tuned Model')
+st.write('Enter some text ')
+
+
+
+text_input = st.text_input('Enter text here')
+
+if st.button('Submit'):
+
+        # Tokenize the text
+    inputs = tokenizer(text_input, return_tensors="pt")
+
+    # Perform prediction
+    output = model(**inputs)
+
+    scores = output[0][0].detach().numpy()
+    scores = softmax(scores)
+    scores_dict = {
+        'Negative': scores[0],
+        'Neutral': scores[1],
+        'Positive': scores[2]
+    }
+    max_key = max(scores_dict, key=scores_dict.get)
+
+    # Get the maximum value
+    sentiment = str(scores_dict[max_key])
+
+    # Display the results
+    st.write(f'Sentiment is {data["sentiment"]}')
+    st.write(f'Score is {max_key}')