deploy

Dockerfile

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+# Use the official Python image from Docker Hub
+FROM python:3.11-slim
+
+# Set the working directory
+WORKDIR /app
+
+# Copy the application files
+COPY app.py /app/
+COPY requirements.txt /app/
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the Streamlit port
+EXPOSE 7860
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

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+---
+title: Sentiment Analysis App
+emoji: 🌍
+colorFrom: indigo
+colorTo: red
+sdk: docker
+pinned: false
+---

app.py

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+import streamlit as st
+import pandas as pd
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import matplotlib.pyplot as plt
+import time
+
+# Load model and tokenizer
+model_name = "tabularisai/multilingual-sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Function for sentiment prediction with progress bar
+def predict_sentiment(texts):
+    sentiments = []
+    sentiment_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
+    progress_bar = st.progress(0)
+    total_texts = len(texts)
+
+    for i, text in enumerate(texts):
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        sentiment = sentiment_map[torch.argmax(probabilities, dim=-1).item()]
+        sentiments.append(sentiment)
+
+        # Update progress bar
+        progress_bar.progress((i + 1) / total_texts)
+        time.sleep(0.1)  # Optional: To better visualize progress
+        
+    return sentiments
+
+# Streamlit UI
+st.title("Sentiment Analysis App")
+st.write("Upload a CSV or Excel file containing text data for sentiment analysis.")
+
+# File upload
+uploaded_file = st.file_uploader("Upload a CSV or Excel file", type=["csv", "xlsx"])
+
+if uploaded_file is not None:
+    # Read file
+    if uploaded_file.name.endswith(".csv"):
+        df = pd.read_csv(uploaded_file)
+    else:
+        df = pd.read_excel(uploaded_file)
+    
+    st.write("Dataset Preview:")
+    st.dataframe(df.head())
+    
+    # Select text column
+    text_column = st.selectbox("Select the text column for analysis", df.columns)
+    
+    if st.button("Analyze Sentiment"):
+        # Get text data
+        texts = df[text_column].astype(str).tolist()
+        
+        # Predict sentiments with progress bar
+        sentiments = predict_sentiment(texts)
+        df["Sentiment"] = sentiments
+        
+        # Display results
+        st.write("Sentiment Analysis Results:")
+        st.dataframe(df[[text_column, "Sentiment"]])
+        
+        # Pie chart of sentiment distribution
+        st.write("Sentiment Distribution:")
+        sentiment_counts = df["Sentiment"].value_counts()
+        fig, ax = plt.subplots()
+        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct="%1.1f%%", startangle=90)
+        ax.axis("equal")
+        st.pyplot(fig)

requirements.txt

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+streamlit
+pandas
+torch
+transformers
+matplotlib