Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import tensorflow as tf
+tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
+
+# Load pre-trained model and tokenizer
+saved_directory = "models/cardiffnlp/twitter-xlm-roberta-base-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(saved_directory)
+model = AutoModelForSequenceClassification.from_pretrained(saved_directory)
+nlp = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
+
+# Define function to analyze sentiment
+def analyze_sentiment(text):
+    result = nlp(text)
+    return result[0]['label'], result[0]['score']
+
+# Streamlit UI
+st.set_page_config(page_title="Sentiment Analysis", layout="wide")
+col1, col2 = st.columns([6, 1])  # Divide the screen into two columns
+
+
+
+with col2:  # Right-aligned column for the logo
+    st.image("https://huggingface.co/spaces/orYx-models/Leadership-sentiment-analyzer/resolve/main/oryx_logo%20(2).png", width=200, use_column_width=False)  # Provide the path to your company logo
+
+with col1:  # Main content area
+
+    # Text titles below the text box
+    st.markdown("This sentiment analysis model serves as a testing prototype, specifically developed for LDS to assess the variability and precision of OrYx Models' sentiment analysis tool.")
+    st.markdown(" ")     
+    st.markdown("All feedback gathered from LDS, including both structured and unstructured data, will be incorporated into the model to enhance its domain specificity and maximize accuracy.")
+
+    
+    st.title("Sentiment Analysis Prototype Tool by orYx Models")
+    user_input = st.text_area("Enter text to analyze:", height=200)
+    
+    submit_button = st.button("Analyze")
+
+    if submit_button and user_input:
+        sentiment, score = analyze_sentiment(user_input)
+        st.markdown("Sentiment Analysis Result:")
+        st.write(f"Sentiment:  {sentiment}")
+        st.write(f"Confidence:  {score*100:.2f}%")