Update app.py

app.py

@@ -22,28 +22,38 @@ txt = st.text_area("Job description")
 data1 = pd.Series(txt, index = ["Job Description"])
 st.dataframe(data1)
 
+
+
 uploaded_files = st.file_uploader(
     "Choose a PDF file(s) and job description as pdf", accept_multiple_files=True, type="pdf"
 )
 
-for uploaded_file in uploaded_files:
-    
-    pdf_reader = PdfReader(uploaded_file)
-    text_data = ""
-    for page in pdf_reader.pages:
-        text_data += page.extract_text()
+if uploaded_files:
+    all_data = []  # Store dictionaries of text and entities for each PDF
+    for uploaded_file in uploaded_files:
+        try:
+            pdf_reader = PdfReader(uploaded_file)
+            text_data = ""
+            for page in pdf_reader.pages:
+                text_data += page.extract_text()
 
-        model = GLiNER.from_pretrained("urchade/gliner_base")
-        labels = ["person", "country", "organization", "time", "role"]
-        entities = model.predict_entities(text_data, labels)
+            model = GLiNER.from_pretrained("urchade/gliner_base")
+            labels = ["person", "country", "organization", "time", "role"]
+            entities = model.predict_entities(text_data, labels)
 
-        entity_dict = {}
-        for label in labels:
-            entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
+            entity_dict = {}
+            for label in labels:
+                entity_dict[label] = [entity["text"] for entity in entities if entity["label"] == label]
 
             data = {"Text": text_data, **entity_dict}
-            df = pd.DataFrame(data)
-            st.dataframe(df)
+            all_data.append(data)
+
+        except Exception as e:
+            st.error(f"Error processing file {uploaded_file.name}: {e}")
+
+    if all_data:
+        df = pd.DataFrame(all_data)
+        st.dataframe(df)