Update app.py

app.py

@@ -38,26 +38,24 @@ all_resumes_text1 = []  # Store the text content of each PDF
 
 if uploaded_files:
     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)
-
-            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}
+        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)
+
+        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}
             
 
 
-            all_resumes_text1.append(data)
-        except Exception as e:
-            st.error(f"Error processing file {uploaded_file.name}: {e}")
+        all_resumes_text1.append(data)
+        
 
     if all_resumes_text1:
         all_documents = [job_description_series.iloc[0]] + all_resumes_text
@@ -96,26 +94,24 @@ all_resumes_text2 = []  # Store the text content of each PDF
 
 if uploaded_files:
     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)
-
-            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}
+        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)
+
+        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}
             
 
 
-            all_resumes_text2.append(data)
-        except Exception as e:
-            st.error(f"Error processing file {uploaded_file.name}: {e}")
+        all_resumes_text2.append(data)
+        
 
     if all_resumes_text2:
         all_documents = [job_description_series.iloc[0]] + all_resumes_text