Application file

dockerfile

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+FROM python:3.9
+#This creates a directory for your app. Do not change anything here
+WORKDIR /app
+
+#This also makes your directory for the cache writable. DO not change anything here 
+RUN mkdir -p /.cache/huggingface/hub && chmod -R 777 /.cache
+
+# This creates a virtual environment for your app on your hugging face. Dont change anything here as well 
+ENV TRANSFORMERS_CACHE /.cache/huggingface/hub
+
+#This copies the requirement to your hugging face account. Do not change anything 
+COPY requirements.txt .
+
+# This copies your streamlit app to your hugging face 
+# change 'sentimentappstreamlit.py' to the actual name of your app. There is one space and a (fullstop)after the name of your app
+COPY sentimentapp.py .
+
+#If you used any picture in your application,first make sure its in the same path as your app.
+#This code copies the picture unto your hugging face. 
+COPY senti.jpg .
+COPY negative-smiley-face.png .
+COPY positive-smiley-face.png .
+COPY neutral-smiley-face.png .
+COPY swipe-swoosh.mp3 .
+
+
+RUN pip3 install --upgrade pip
+
+RUN pip3 install -r requirements.txt
+
+
+
+CMD ["streamlit","run","sentimentapp.py", "--server.address", "0.0.0.0", "--server.port", "7860", "--browser.serverAddress", "Adoley/personal_app.hf.space", "--browser.serverAddress","0.0.0.0:7860"]

requirements.txt

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+streamlit==0.93.0
+transformers==4.11.3
+torch==1.9.0
+pandas==1.3.4
+altair==4.1.0
+textblob==0.15.3
+vaderSentiment==3.5.1
+Pillow==8.4.0

sentimentapp.py

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+import pandas as pd
+import numpy as np
+import streamlit as st
+import altair as alt
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from PIL import Image
+import base64
+
+
+
+
+
+
+# Functions
+def main():
+    st.title("Sentiment Analysis App")
+    st.subheader("Reformation Team Project")
+
+    
+
+    st.image("senti.jpg")
+
+    # Define the available models
+    models = {
+        "ROBERTA": "Adoley/covid-tweets-sentiment-analysis-roberta-model",
+        "BERT": "Adoley/covid-tweets-sentiment-analysis",
+        "DISTILBERT": "Adoley/covid-tweets-sentiment-analysis-distilbert-model"
+    }
+
+    menu = ["Home", "About"]
+    choice = st.sidebar.selectbox("Menu", menu)
+
+    how_to_use = """
+## How to Use
+
+1. Enter your text in the input box.
+2. Click the **Analyze Sentiment** button.
+3. Wait for the app to process the text and display the sentiment analysis results.
+4. Explore the sentiment scores and visualization provided.
+"""
+
+    # 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")
+
+       
+
+        col1, col2 = st.columns(2)
+        if submit_button:
+            # Display sound-effect
+            st.info("🔮 Abracadabra! Your report has been submitted!")
+            sound_file = 'C:/Users/viole/OneDrive/Documents/streamlit2/swipe-swoosh.mp3'
+            st.audio(sound_file, format='audio/wav')
+
+            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-smiley-face.png")
+                elif predicted_class == 1:
+                    st.markdown("Sentiment: Neutral :😐:")
+                    st.image("neutral-smiley-face.png")
+                else:
+                    st.markdown("Sentiment: Negative :angry:")
+                    st.image("negative-smiley-face.png")
+
+            
+
+            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)
+
+           
+
+            # Create the Altair chart
+            chart = alt.Chart(results_df).mark_bar(width=50).encode(
+                x="Sentiment Class",
+                y="Score",
+                color="Sentiment Class"
+            )
+
+            # Display the chart
+            with col2:
+                st.altair_chart(chart, use_container_width=True)
+                st.write(results_df)
+
+    else:
+        st.subheader("About")
+        st.write("This marvelous sentiment analysis NLP app, crafted with love by the brilliant minds of Team Reformation, dives into the realm of Covid-19 tweets. Armed with a pre-trained model, it fearlessly predicts the sentiment lurking within the depths of your text. Brace yourself for an adventure of teamwork and collaboration, as we embark on a quest to unravel the sentiments that dwell within the tweetsphere!")
+
+
+
+if __name__ == "__main__":
+    main()