Create app.py

app.py

@@ -0,0 +1,59 @@
+import streamlit as st
+from transformers import BertForSequenceClassification, BertTokenizerFast, pipeline
+
+# Load the BERT model and tokenizer
+model_path = "./"
+model = BertForSequenceClassification.from_pretrained(model_path)
+tokenizer = BertTokenizerFast.from_pretrained(model_path)
+nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+# Function to update sentiment analysis
+def analyze_sentiment(text):
+    if text.strip():
+        result = nlp(text)
+        label = result[0]['label']
+        score = result[0]['score']
+        return label, score
+    else:
+        return None, None
+
+# Function to get emoji based on emotion
+def get_emoji(label):
+    if label == "Anger":
+        return "😠"
+    elif label == "Astonished":
+        return "😲"
+    elif label == "Optimistic":
+        return "😊"
+    elif label == "Sadness":
+        return "😢"
+    else:
+        return "🙂"
+
+# Streamlit app
+st.set_page_config(page_title="G-Bert: Emotion Analysis", page_icon="😊", layout="centered")
+
+# Title
+st.title("G-Bert: Emotion Analysis")
+
+# Text input
+st.write("Enter some text below, and G-Bert will analyze its emotion!")
+text = st.text_area("Input Text", height=200)
+
+# Analyze button
+if st.button("Analyze Emotion"):
+    if text.strip():
+        label, score = analyze_sentiment(text)
+        if label and score:
+            emoji = get_emoji(label)
+            st.subheader(f"Emotion: {label}")
+            st.markdown(f"**Confidence Score:** {score:.2f}")
+            st.markdown(f"{emoji} **{label}**")
+        else:
+            st.error("Something went wrong with the analysis.")
+    else:
+        st.warning("Please enter some text to analyze.")
+
+# Footer
+st.markdown("---")
+st.markdown("Built with ❤️ using Hugging Face and Streamlit")