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requirements.txt

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+streamlit
+transformers
+torch

streamlit_app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load models
+
+# Distilled Sentiment Classifier
+# Link: https://huggingface.co/lxyuan/distilbert-base-multilingual-cased-sentiments-student
+distilled_sentiment_classifier = pipeline(
+    model="lxyuan/distilbert-base-multilingual-cased-sentiments-student", 
+    return_all_scores=True
+)
+
+# Emotion Classifier
+# Link: https://huggingface.co/SamLowe/roberta-base-go_emotions
+emotion_text_classifier = pipeline("text-classification", model="SamLowe/roberta-base-go_emotions")
+
+# Named Entity Recognition
+# Link: https://huggingface.co/mdarhri00/named-entity-recognition
+named_entity_classifier = pipeline("token-classification", model="mdarhri00/named-entity-recognition")
+
+# Toxicity Classifier
+# Link: https://huggingface.co/s-nlp/roberta_toxicity_classifier
+toxicity_classifier = pipeline("text-classification", model="s-nlp/roberta_toxicity_classifier")
+
+# Streamlit app
+def main():
+    st.title("HuggingFace Model Demo App")
+
+    # User input for text
+    user_text = st.text_area("Enter some text:")
+
+    if user_text:
+        # Available Models
+        # Sentiment Analysis
+        sentiment_checkbox = st.checkbox("Sentiment Analysis")
+
+        # Emotion Analysis
+        emotion_checkbox = st.checkbox("Emotion Analysis")
+
+        # Named Entity Recognition
+        ner_checkbox = st.checkbox("Named Entity Recognition")
+
+        # Toxicity Analysis
+        toxicity_checkbox = st.checkbox("Toxicity Analysis")
+
+        # Run custom and display outputs
+        st.header("Function Outputs:")
+
+        if sentiment_checkbox:
+            st.subheader("Sentiment Analysis:")
+            
+            # Parse JSON data
+            data = distilled_sentiment_classifier(user_text)
+            
+            # Extract and display label and score values
+            for labels_and_scores in data:
+                for entry in labels_and_scores:
+                    label = entry["label"]
+                    score = entry["score"]
+                    st.write(f"Label: {label}, Score: {score}")
+
+
+        if emotion_checkbox:
+            st.subheader("Emotion Analysis:")
+            
+            # Parse JSON data
+            data = emotion_text_classifier(user_text)
+            
+            # Extract and display label and score values
+            for labels_and_scores in data:
+                for entry in labels_and_scores:
+                    label = entry["label"]
+                    score = entry["score"]
+                    st.write(f"Label: {label}, Score: {score}")
+
+        if ner_checkbox:
+            st.subheader("Named Entity Recognition:")
+            
+            # Parse JSON data
+            data = named_entity_classifier(user_text)
+
+            # Extract and display data
+            for entry in data:
+                entity_group = entry["entity_group"]
+                score = entry["score"]
+                word = entry["word"]
+                start = entry["start"]
+                end = entry["end"]
+                st.write(f"Word: {word}, Entity Group: {entity_group}, Score: {score}, Start: {start}, End: {end}")
+
+        if toxicity_checkbox:
+            st.subheader("Toxicity Analysis:")
+            
+            # Parse JSON data
+            data = toxicity_classifier(user_text)
+
+            # Extract and display label and score values
+            for labels_and_scores in data:
+                for entry in labels_and_scores:
+                    label = entry["label"]
+                    score = entry["score"]
+                    st.write(f"Label: {label}, Score: {score}")
+
+if __name__ == "__main__":
+    main()