Update app.py

app.py

@@ -110,40 +110,43 @@ if st.session_state['upload_count'] < max_attempts:
     if uploaded_files:
         st.session_state['upload_count'] += 1
         with st.spinner("Wait for it...", show_time=True):
-            time.sleep(2)
-            pdf_reader = PdfReader(uploaded_files)
-            text_data = ""
-            for page in pdf_reader.pages:
-                text_data += page.extract_text()
-
-            data = pd.Series(text_data, name='Text')
-            frames = [job_desc, data]
-            result = pd.concat(frames)
-            model = GLiNER.from_pretrained("urchade/gliner_base")
-            labels = ["person", "country", "organization", "role", "skills"]
-            entities = model.predict_entities(text_data, labels)
-            df = pd.DataFrame(entities)
-            st.subheader("Applicant's Profile", divider = "orange")
-            fig = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
+    time.sleep(2)
+    pdf_reader = PdfReader(uploaded_files)
+    text_data = ""
+    for page in pdf_reader.pages:
+        text_data += page.extract_text()
+
+    job_desc_series = pd.Series([job_desc], name='Text')  # Convert job_desc to a Series
+    data = pd.Series([text_data], name='Text')        # Ensure text_data is also a Series
+
+    frames = [job_desc_series, data]
+    result = pd.concat(frames, ignore_index=True)  # Concatenate along rows, reset index
+
+    model = GLiNER.from_pretrained("urchade/gliner_base")
+    labels = ["person", "country", "organization", "role", "skills"]
+    entities = model.predict_entities(text_data, labels)
+    df = pd.DataFrame(entities)
+    st.subheader("Applicant's Profile", divider = "orange")
+    fig = px.treemap(entities, path=[px.Constant("all"), 'text', 'label'],
                              values='score', color='label')
-            fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
-            st.plotly_chart(fig, key="figure 1")
+    fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+    st.plotly_chart(fig, key="figure 1")
 
             
-            job_embedding = model.encode([job_desc])
-            resume_embedding = model.encode([text_data])
+    job_embedding = model.encode([job_desc])
+    resume_embedding = model.encode([text_data])
 
                 
-            similarity_score = model.similarity(job_embedding, resume_embedding)[0][0]
+    similarity_score = model.similarity(job_embedding, resume_embedding)[0][0]
             
                   
-            st.subheader("Similarity between Applicant's Profile and Job Description", divider = "orange")
+    st.subheader("Similarity between Applicant's Profile and Job Description", divider = "orange")
 
-            fig = px.imshow(similarity_score, text_auto=True,
+    fig = px.imshow(similarity_score, text_auto=True,
                             labels=dict(x="Keyword similarity", y="Resumes", color="Productivity"),
                             x=['Resume', 'Jon Description'],
                             y=['Resume', 'Job Description'])
-            st.plotly_chart(fig, key="figure 2")
+    st.plotly_chart(fig, key="figure 2")
 else:
     st.warning(f"Maximum upload attempts has been reached ({max_attempts}).")
     if 'upload_count' in st.session_state and st.session_state['upload_count'] > 0: