Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the Hugging Face pipelines
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
+
+# Define the categories for customer feedback
+CATEGORIES = ["Pricing", "Feature", "Customer Service", "Delivery", "Quality"]
+
+# Streamlit app UI
+st.title("Customer Feedback Categorization with Sentiment Analysis")
+st.markdown(
+    """
+    This app uses Hugging Face models to detect the topics and intent of customer feedback
+    and determine the sentiment (positive or negative) for each relevant category.
+    A single feedback may belong to multiple categories, such as Pricing, Feature, and Customer Service.
+    """
+)
+
+# Input text box for customer feedback
+feedback_input = st.text_area(
+    "Enter customer feedback:",
+    placeholder="Type your feedback here...",
+    height=200
+)
+
+# Confidence threshold for zero-shot classification
+threshold = st.slider(
+    "Confidence Threshold",
+    min_value=0.0,
+    max_value=1.0,
+    value=0.2,
+    step=0.05,
+    help="Categories with scores above this threshold will be displayed."
+)
+
+# Classify button
+if st.button("Classify Feedback"):
+    if not feedback_input.strip():
+        st.error("Please provide valid feedback text.")
+    else:
+        # Perform zero-shot classification
+        classification_result = classifier(feedback_input, CATEGORIES)
+
+        # Filter categories with scores above the threshold
+        relevant_categories = {
+            label: round(score, 4)
+            for label, score in zip(classification_result["labels"], classification_result["scores"])
+            if score >= threshold
+        }
+
+        # Check if there are any relevant categories
+        if relevant_categories:
+            st.subheader("Categorized Feedback with Sentiment Analysis")
+
+            for category, score in relevant_categories.items():
+                # Extract the part of feedback relevant to the category for sentiment analysis
+                sentiment_result = sentiment_analyzer(feedback_input)
+                sentiment_label = sentiment_result[0]["label"]
+                sentiment_score = round(sentiment_result[0]["score"], 4)
+
+                # Display the category, confidence score, and sentiment result
+                st.write(f"### **{category}**")
+                st.write(f"- Confidence: {score}")
+                st.write(f"- Sentiment: {sentiment_label} (Score: {sentiment_score})")
+        else:
+            st.warning("No categories matched the selected confidence threshold.")