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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}")
+

requirements.txt

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+altair==5.0.1
+attrs==23.1.0
+blinker==1.6.2
+cachetools==5.3.1
+certifi==2023.5.7
+charset-normalizer==3.1.0
+click==8.1.3
+decorator==5.1.1
+filelock==3.12.2
+fsspec==2023.6.0
+gitdb==4.0.10
+GitPython==3.1.31
+huggingface-hub==0.15.1
+idna==3.4
+importlib-metadata==6.7.0
+Jinja2==3.1.2
+jsonschema==4.17.3
+markdown-it-py==3.0.0
+MarkupSafe==2.1.3
+mdurl==0.1.2
+mpmath==1.3.0
+networkx==3.1
+numpy==1.25.0
+packaging==23.1
+pandas==2.0.3
+Pillow==9.5.0
+protobuf==4.23.3
+pyarrow==12.0.1
+pydeck==0.8.1b0
+Pygments==2.15.1
+Pympler==1.0.1
+pyrsistent==0.19.3
+python-dateutil==2.8.2
+pytz==2023.3
+pytz-deprecation-shim==0.1.0.post0
+PyYAML==6.0
+regex==2023.6.3
+requests==2.31.0
+rich==13.4.2
+safetensors==0.3.1
+six==1.16.0
+smmap==5.0.0
+streamlit==1.24.0
+sympy==1.12
+tenacity==8.2.2
+tokenizers==0.13.3
+toml==0.10.2
+toolz==0.12.0
+torch==2.0.1
+tornado==6.3.2
+tqdm==4.65.0
+transformers==4.30.2
+typing_extensions==4.7.1
+tzdata==2023.3
+tzlocal==4.3.1
+urllib3==2.0.3
+validators==0.20.0
+watchdog==3.0.0
+zipp==3.15.0