fixed config

README.md

@@ -1,15 +1,14 @@
 ---
-title: Resume Ranker
-emoji: 🏢
-colorFrom: green
-colorTo: pink
-sdk: gradio
-sdk_version: 3.33.1
+title: Resume Ranking
+emoji: 📊
+colorFrom: yellow
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.21.0
 app_file: app.py
 pinned: false
 ---
 
-
 # resume-ranker
 <hr>
 

app.py

@@ -14,50 +14,43 @@ def inference(query, files, embedding_type):
     prob_per_documents = {result['name']: result['similarity'] for result in results}
     return prob_per_documents
 
-def main():
-    sample_files = [
-        "documents/business.pdf",
-        "documents/data_science.pdf",
-    ]        
-
-    sample_job_descriptions = {
-        "Software Engineer": """We are looking for a software engineer with experience in Python and web development. The ideal candidate should have a strong background in building scalable and robust applications. Knowledge of frameworks such as Flask and Django is a plus. Experience with front-end technologies like HTML, CSS, and JavaScript is desirable. The candidate should also have a good understanding of databases and SQL. Strong problem-solving and communication skills are required for this role.
-        """,
-        "Data Scientist": """We are seeking a data scientist with expertise in machine learning and statistical analysis. The candidate should have a solid understanding of data manipulation, feature engineering, and model development. Proficiency in Python and popular data science libraries such as NumPy, Pandas, and Scikit-learn is required. Experience with deep learning frameworks like TensorFlow or PyTorch is a plus. Strong analytical and problem-solving skills are essential for this position.
-        """
-    }    
-    st.sidebar.header("Sample Files")
-    for sample_file in sample_files:
-        st.sidebar.markdown(f"[{sample_file}](./sample_files/{sample_file})")
-
-    st.sidebar.header("Sample Job Descriptions")
-    selected_job = st.sidebar.selectbox("Select a job description", list(sample_job_descriptions.keys()))
-    st.sidebar.markdown("```")
-    st.sidebar.code(sample_job_descriptions[selected_job])
-    st.title("👨🏼‍🎓Resume Ranker ")
-    
-    query = st.text_area("Job Description", height=200, value=sample_job_descriptions[selected_job])
-    uploaded_files = st.file_uploader("Upload Resume", accept_multiple_files=True, type=["txt", "pdf"])
-    embedding_type = st.selectbox("Embedding Type", ["bert", "minilm", "tfidf"])
-    
-    if st.button("Submit"):
-        if not query:
-            st.warning("Please enter a job description.")
-        elif not uploaded_files:
-            st.warning("Please upload one or more resumes.")
-        else:
-            with st.spinner("Processing..."):
-                results = inference(query, uploaded_files,embedding_type)
-            st.subheader("Results")
-            for document, similarity in results.items():
-                # make similiarty round to 2 decimal place
-                if similarity >= 1:
-                    similarity = round(similarity, 2)
-                st.write(f"- {document}:")
-                st.progress(similarity, text=f"{similarity:.2%}")
-
-
-
-    
-if __name__ == '__main__':
-    main()
+sample_files = [
+    "documents/business.pdf",
+    "documents/data_science.pdf",
+]        
+
+sample_job_descriptions = {
+    "Software Engineer": """We are looking for a software engineer with experience in Python and web development. The ideal candidate should have a strong background in building scalable and robust applications. Knowledge of frameworks such as Flask and Django is a plus. Experience with front-end technologies like HTML, CSS, and JavaScript is desirable. The candidate should also have a good understanding of databases and SQL. Strong problem-solving and communication skills are required for this role.
+    """,
+    "Data Scientist": """We are seeking a data scientist with expertise in machine learning and statistical analysis. The candidate should have a solid understanding of data manipulation, feature engineering, and model development. Proficiency in Python and popular data science libraries such as NumPy, Pandas, and Scikit-learn is required. Experience with deep learning frameworks like TensorFlow or PyTorch is a plus. Strong analytical and problem-solving skills are essential for this position.
+    """
+}    
+st.sidebar.header("Sample Files")
+for sample_file in sample_files:
+    st.sidebar.markdown(f"[{sample_file}](./sample_files/{sample_file})")
+
+st.sidebar.header("Sample Job Descriptions")
+selected_job = st.sidebar.selectbox("Select a job description", list(sample_job_descriptions.keys()))
+st.sidebar.markdown("```")
+st.sidebar.code(sample_job_descriptions[selected_job])
+st.title("👨🏼‍🎓Resume Ranker ")
+
+query = st.text_area("Job Description", height=200, value=sample_job_descriptions[selected_job])
+uploaded_files = st.file_uploader("Upload Resume", accept_multiple_files=True, type=["txt", "pdf"])
+embedding_type = st.selectbox("Embedding Type", ["bert", "minilm", "tfidf"])
+
+if st.button("Submit"):
+    if not query:
+        st.warning("Please enter a job description.")
+    elif not uploaded_files:
+        st.warning("Please upload one or more resumes.")
+    else:
+        with st.spinner("Processing..."):
+            results = inference(query, uploaded_files,embedding_type)
+        st.subheader("Results")
+        for document, similarity in results.items():
+            # make similiarty round to 2 decimal place
+            if similarity >= 1:
+                similarity = round(similarity, 2)
+            st.write(f"- {document}:")
+            st.progress(similarity, text=f"{similarity:.2%}")