create app.py

app.py

@@ -0,0 +1,128 @@
+import pandas as pd
+import numpy as np
+import streamlit as st
+import plotly.express as px
+import plotly.io as pio
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from PIL import Image
+import base64
+
+
+# Define the "How to Use" message
+how_to_use = """
+**How to Use**
+1. Select a model from the dropdown menu
+2. Enter text in the text area
+3. Click the 'Analyze' button to get the predicted sentiment of the text
+"""
+
+# Functions
+def main():
+    st.title("Covid Tweets Sentiment Analysis NLP App")
+    st.subheader("Team Harmony Project")
+
+    # Add the cover image
+    #st.markdown(f'<img src="data:image/jpeg;base64,{image_base64}" alt="Cover Image">', unsafe_allow_html=True)
+
+    st.image("Cover_image.jpg")
+
+    # Define the available models
+    models = {
+        "ROBERTA": "Abubakari/finetuned-Sentiment-classfication-ROBERTA-model",
+        "BERT": "Abubakari/finetuned-Sentiment-classfication-BERT-model",
+        "DISTILBERT": "Abubakari/finetuned-Sentiment-classfication-DISTILBERT-model"
+    }
+
+    menu = ["Home", "About"]
+    choice = st.sidebar.selectbox("Menu", menu)
+
+    # Add the "How to Use" message to the sidebar
+    st.sidebar.markdown(how_to_use)
+
+    if choice == "Home":
+        st.subheader("Home")
+
+        # Add a dropdown menu to select the model
+        model_name = st.selectbox("Select a model", list(models.keys()))
+
+        with st.form(key="nlpForm"):
+            raw_text = st.text_area("Enter Text Here")
+            submit_button = st.form_submit_button(label="Analyze")
+
+        # Layout
+        col1, col2 = st.columns(2)
+        if submit_button:
+            # Display balloons
+            st.balloons()
+            with col1:
+                st.info("Results")
+                tokenizer = AutoTokenizer.from_pretrained(models[model_name])
+                model = AutoModelForSequenceClassification.from_pretrained(models[model_name])
+
+                # Tokenize the input text
+                inputs = tokenizer(raw_text, return_tensors="pt")
+
+                # Make a forward pass through the model
+                outputs = model(**inputs)
+
+                # Get the predicted class and associated score
+                predicted_class = outputs.logits.argmax().item()
+                score = outputs.logits.softmax(dim=1)[0][predicted_class].item()
+
+                # Compute the scores for all sentiments
+                positive_score = outputs.logits.softmax(dim=1)[0][2].item()
+                negative_score = outputs.logits.softmax(dim=1)[0][0].item()
+                neutral_score = outputs.logits.softmax(dim=1)[0][1].item()
+
+                # Compute the confidence level
+                confidence_level = np.max(outputs.logits.detach().numpy())
+
+                # Print the predicted class and associated score
+                st.write(f"Predicted class: {predicted_class}, Score: {score:.3f}, Confidence Level: {confidence_level:.2f}")
+
+                # Emoji
+                if predicted_class == 2:
+                    st.markdown("Sentiment: Positive :smiley:")
+                    st.image("Positive_sentiment.jpg")
+                elif predicted_class == 1:
+                    st.markdown("Sentiment: Neutral :😐:")
+                    st.image("Neutral_sentiment.jpg")
+                else:
+                    st.markdown("Sentiment: Negative :angry:")
+                    st.image("Negative_sentiment2.png")
+
+            # Create the results DataFrame
+            # Define an empty DataFrame with columns
+
+            results_df = pd.DataFrame(columns=["Sentiment Class", "Score"])
+
+            # Create a DataFrame with scores for all sentiments
+            all_scores_df = pd.DataFrame({
+            'Sentiment Class': ['Positive', 'Negative', 'Neutral'],
+            'Score': [positive_score, negative_score, neutral_score]
+            })
+
+            # Concatenate the two DataFrames
+
+            results_df = pd.concat([results_df, all_scores_df], ignore_index=True)
+
+            #results_df = pd.DataFrame({
+            #    "Sentiment Class": [predicted_class],
+            #    "Score": [score]
+            #})
+
+            # Create the Plotly figure
+            fig = px.bar(results_df, x="Sentiment Class", y="Score", color="Sentiment Class")
+
+            with col2:
+                st.plotly_chart(fig, use_container_width=True)
+                st.write(results_df)
+
+    else:
+        st.subheader("About")
+        st.write("This is a sentiment analysis NLP app developed by Team Harmony for analyzing tweets related to Covid-19. It uses a pre-trained model to predict the sentiment of the input text. The app is part of a project to promote teamwork and collaboration among developers.")
+
+
+
+if __name__ == "__main__":
+    main()