Create mile2.py

mile2.py

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+import streamlit as st
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+import torch
+from transformers import pipeline
+
+# Set up the Streamlit app
+st.title("Sentiment Analysis App")
+st.write('Welcome to my Sentiment Analysis app!')
+
+#subtitle
+st.markdown("Sentiment Analysis App using 'streamlit' hosted on hugging spaces ")
+st.markdown("")
+
+user_input = st.text_area("Enter your text", value="")
+form = st.form(key='sentiment-form')
+submit = form.form_submit_button('Submit')
+
+classifier = pipeline(model="distilbert-base-uncased-finetuned-sst-2-english")
+classifier("I've been waiting for HuggingFAcecourse my whole life.")
+
+classifier = pipeline(model="distilbert-base-uncased-finetuned-sst-2-english") 
+result = classifier(user_input)[0]    
+label = result['label']    
+score = result['score']
+
+if submit:
+    classifier = pipeline(model="distilbert-base-uncased-finetuned-sst-2-english")
+    result = classifier(user_input)[0]
+    label = result['label']
+    score = result['score']
+if label == 'POSITIVE':
+        st.success(f'{label} sentiment (score: {score})')
+else:
+    st.error(f'{label} sentiment (score: {score})')
+
+# Load the sentiment analysis model and tokenizer
+model_name = "textattack/bert-base-uncased-SST-2"
+model2 = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+
+# Model selection
+model_options = {
+    "BERT-base-uncased-SST-2": "textattack/bert-base-uncased-SST-2",
+    "BERT-base-cased-finetuned-mrpc": "bert-base-cased-finetuned-mrpc"
+}
+model_name = st.selectbox("Select a pretrained model", list(model_options.keys()))
+model_path = model_options[model_name]
+
+# Sentiment analysis
+if st.button("Analyze"):
+    if user_input.strip() == "":
+        st.warning("Please enter some text.")
+    else:
+        # Tokenize input text
+        inputs = tokenizer.encode_plus(user_input, return_tensors="pt", padding=True, truncation=True)
+
+        # Perform sentiment analysis
+        with torch.no_grad():
+            outputs = model2(**inputs)
+            logits = outputs.logits
+            predicted_label = torch.argmax(logits, dim=1).item()
+
+        sentiment = "Positive" if predicted_label == 1 else "Negative"
+        st.success(f"The sentiment of the text is: {sentiment}")
+