Update app.py

app.py

@@ -1,41 +1,42 @@
-import streamlit as st
-import json
-import torch
-from transformers import pipeline
-from datasets import load_metric
-
-# Load evaluation metric
-rouge = load_metric("rouge")
-
-# Load the summarization model
-summarizer = pipeline("summarization", model="facebook/bart-base")
-
-st.title("📝 Text Summarization with Hugging Face & Streamlit")
-
-# User input
-user_input = st.text_area("Enter your text here:", "")
-
-if st.button("Summarize"):
-    if user_input:
-        # Generate summary
-        summary = summarizer(user_input, max_length=50, min_length=5, do_sample=False)[0]["summary_text"]
-        st.subheader("Generated Summary:")
-        st.write(summary)
-
-        # Evaluate with a dummy reference summary
-        reference_summary = "Example reference summary for evaluation"
-        score = rouge.compute(predictions=[summary], references=[reference_summary])
-        
-        st.subheader("ROUGE Scores:")
-        st.json(score)
-    else:
-        st.warning("⚠️ Please enter text to summarize!")
-
-# Display latest evaluation results
-st.subheader("Latest Evaluation Results:")
-try:
-    with open("evaluation_results.json", "r") as f:
-        results = json.load(f)
-    st.json(results)
-except FileNotFoundError:
-    st.write("No evaluation results found.")
+import streamlit as st
+import json
+import torch
+from transformers import pipeline
+import evaluate
+
+
+# Load evaluation metric
+rouge = evaluate.load("rouge")
+
+# Load the summarization model
+summarizer = pipeline("summarization", model="facebook/bart-base")
+
+st.title("📝 Text Summarization with Hugging Face & Streamlit")
+
+# User input
+user_input = st.text_area("Enter your text here:", "")
+
+if st.button("Summarize"):
+    if user_input:
+        # Generate summary
+        summary = summarizer(user_input, max_length=50, min_length=5, do_sample=False)[0]["summary_text"]
+        st.subheader("Generated Summary:")
+        st.write(summary)
+
+        # Evaluate with a dummy reference summary
+        reference_summary = "Example reference summary for evaluation"
+        score = rouge.compute(predictions=[summary], references=[reference_summary])
+        
+        st.subheader("ROUGE Scores:")
+        st.json(score)
+    else:
+        st.warning("⚠️ Please enter text to summarize!")
+
+# Display latest evaluation results
+st.subheader("Latest Evaluation Results:")
+try:
+    with open("evaluation_results.json", "r") as f:
+        results = json.load(f)
+    st.json(results)
+except FileNotFoundError:
+    st.write("No evaluation results found.")