sms

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/
+
+# Create a writable cache directory for Hugging Face
+ENV HF_HOME=/app/hf_cache
+RUN mkdir -p /app/hf_cache
+
+# Set permissions for the cache directory
+RUN chmod -R 777 /app/hf_cache
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Create a non-root user and switch to it
+RUN useradd -m appuser
+USER appuser
+
+# Expose the Streamlit port
+EXPOSE 7860
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

app.py

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+import streamlit as st
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import matplotlib.pyplot as plt
+
+# Load model and tokenizer
+model_name = "tabularisai/multilingual-sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+def predict_sentiment(texts):
+    inputs = tokenizer(texts, 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_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
+    return [sentiment_map[p] for p in torch.argmax(probabilities, dim=-1).tolist()]
+
+# Streamlit UI
+st.title("Sentiment Analysis App")
+st.write("Upload an Excel file containing text data, and we'll analyze its sentiment.")
+
+uploaded_file = st.file_uploader("Upload Excel File", type=["xlsx", "xls"])
+
+if uploaded_file is not None:
+    df = pd.read_excel(uploaded_file)
+    st.write("Preview of Uploaded Data:")
+    st.dataframe(df.head())
+    
+    text_column = st.selectbox("Select the column containing text", df.columns)
+    
+    if st.button("Analyze Sentiment"):
+        df["Sentiment"] = predict_sentiment(df[text_column].astype(str).tolist())
+        
+        # Display results
+        st.write("Sentiment Analysis Results:")
+        st.dataframe(df[[text_column, "Sentiment"]])
+        
+        # Pie chart
+        sentiment_counts = df["Sentiment"].value_counts()
+        fig, ax = plt.subplots()
+        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', colors=["red", "yellow", "pink", "lightgreen", "green"])
+        ax.set_title("Sentiment Distribution")
+        st.pyplot(fig)
+        
+        # Table display for sentiment analysis results
+        st.write("Detailed Sentiment Table:")
+        st.table(df[[text_column, "Sentiment"]])
+        
+        # Download option
+        st.download_button("Download Results", df.to_csv(index=False).encode('utf-8'), "sentiment_results.csv", "text/csv")

requirements.txt

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+streamlit
+pandas
+torch
+transformers
+matplotlib