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app.py

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+import streamlit as st
+import pandas as pd
+from emotion_analysis import get_emotion
+import base64
+
+
+def read_data(file_path):
+    file_extension = file_path.split('.')[-1].lower()
+    
+    if file_extension == 'xlsx' or file_extension == 'xls':
+        data = pd.read_excel(file_path)
+    elif file_extension == 'csv':
+        data = pd.read_csv(file_path)
+    else:
+        raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.")
+    
+    return data
+
+
+# Streamlit app
+def main():
+    st.title("Text Emotion Detection")
+    menu = ["Input Text", "Batch Processing"]
+    option = st.sidebar.radio("Select an option", menu)
+
+
+    if option == "Input Text":
+        text = st.text_area("Enter your text:")
+        if st.button("Submit"):
+            if text.strip() != "":
+                emotion_detail, confidence_score = get_emotion(text)
+                st.write("Detected Emotion")
+                st.write(f"{emotion_detail[0]} - {confidence_score}")
+            else:
+                st.write("Please enter some text.")
+
+    elif option == "Batch Processing":
+        uploaded_file = st.file_uploader("Upload CSV or Excel file", type=["csv", "xlsx"])
+        
+        if uploaded_file is not None:
+            file_name = uploaded_file.name
+            file_extension = file_name.split('.')[-1].lower()
+            file_name = uploaded_file.name
+            if file_extension == 'xlsx' or file_extension == 'xls':
+                dataframe = pd.read_excel(uploaded_file)
+            elif file_extension == 'csv':
+                dataframe = pd.read_csv(uploaded_file)
+            else:
+                raise ValueError("Unsupported file format. Only Excel (xlsx, xls) and CSV (csv) files are supported.")
+            # dataframe = pd.read_excel(uploaded_file)
+            if "text" not in dataframe.columns:
+                st.write("CSV file should have a 'text' column.")
+            else:
+                dataframe["emotion"], dataframe["confidence"] = zip(*dataframe["text"].map(get_emotion))
+                st.write("Detected Emotions")
+                st.write(dataframe)
+                # Download button
+                csv = dataframe.to_csv(index=False)
+                b64 = base64.b64encode(csv.encode()).decode()  # Convert DataFrame to CSV string
+                href = f'<a href="data:file/csv;base64,{b64}" download="processed_data.csv">Download</a>'
+                st.markdown(href, unsafe_allow_html=True)
+        else:
+            pass
+
+if __name__ == '__main__':
+    main()

emotion_analysis.py

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+
+from transformers import RobertaTokenizerFast, TFRobertaForSequenceClassification, pipeline
+
+tokenizer = RobertaTokenizerFast.from_pretrained("arpanghoshal/EmoRoBERTa")
+model = TFRobertaForSequenceClassification.from_pretrained("arpanghoshal/EmoRoBERTa")
+
+emotion = pipeline('sentiment-analysis', 
+                    model='arpanghoshal/EmoRoBERTa')
+
+
+def get_emotion(text):
+    emotion_labels = emotion(text)
+    emotion_detail = [item['label'] for item in emotion_labels]
+    print("The detected emotion is:", emotion_detail)
+    confidence_score = str(round([item['score'] for item in emotion_labels][0]*100, 2)) + "%"
+    print("The confidence score is:", confidence_score)
+    return emotion_detail[0], confidence_score