Update app.py

app.py

@@ -1,17 +1,41 @@
+# import streamlit as st
+# from transformers import pipeline
+
+# # Load NER model
+# ner_model = pipeline("ner", model="has-abi/distilBERT-finetuned-resumes-sections")
+
+# # Create Streamlit app
+# st.title("Named Entity Recognition with Hugging Face models")
+
+# # Get user input
+# text_input = st.text_input("Enter some text:")
+
+# # Run NER on user input
+# if text_input:
+#     results = ner_model(text_input)
+#     for result in results:
+#         st.write(f"{result['word']}: {result['entity']}")
 import streamlit as st
 from transformers import pipeline
 
-# Load NER model
-ner_model = pipeline("ner", model="has-abi/distilBERT-finetuned-resumes-sections")
+# Set up Resuméner pipeline
+summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-6-6")
 
 # Create Streamlit app
-st.title("Named Entity Recognition with Hugging Face models")
+st.title("Resuméner")
+st.write("Upload your resume below to generate a summary.")
+
+# Upload resume file
+uploaded_file = st.file_uploader("Choose a file")
 
-# Get user input
-text_input = st.text_input("Enter some text:")
+if uploaded_file is not None:
+    # Read resume file contents
+    resume_text = uploaded_file.read().decode("utf-8")
+    
+    # Generate summary using Resuméner pipeline
+    summary = summarizer(resume_text, max_length=100, min_length=30, do_sample=False)[0]['summary_text']
+    
+    # Display summary
+    st.write("Summary:")
+    st.write(summary)
 
-# Run NER on user input
-if text_input:
-    results = ner_model(text_input)
-    for result in results:
-        st.write(f"{result['word']}: {result['entity']}")