Update app.py

app.py

@@ -1,5 +1,4 @@
 import streamlit as st
-import os
 from cv_analyzer import analyze_cv
 from cv_quality import CV
 from get_supabase import Supabase
@@ -15,23 +14,18 @@ supabase_client = Supabase().init_supabase_client()
 BUCKET_NAME = "CVs UX"
 SUPABASE_PROJECT_ID = "abjtqzgnrtsikkqgnqeg"
 
-uploaded_file = st.file_uploader("Choose a CV file", type=['pdf', 'docx', 'txt'])
+# Get list of files from the Supabase bucket
+files = supabase_client.storage.from_(BUCKET_NAME).list()
+file_names = [file['name'] for file in files]
 
-if uploaded_file is not None:
-    with st.spinner('Uploading and analyzing CV...'):
-        # Upload file to Supabase storage
-        file_path = f"{uploaded_file.name}"
-        
-        with uploaded_file as f:
-            supabase_client.storage.from_(BUCKET_NAME).upload(
-                file=f,
-                path=file_path,
-                file_options={"cache-control": "3600", "upsert": "true"}
-            )
+# Create a dropdown to select the file
+selected_file = st.selectbox("Select a CV to analyze", file_names)
 
-        # Construct the public URL of the uploaded file
+if selected_file:
+    with st.spinner('Analyzing CV...'):
+        # Construct the public URL of the selected file
         timestamp = datetime.utcnow().strftime("%Y-%m-%dT%H:%M:%S.%fZ")
-        file_url = f"https://{SUPABASE_PROJECT_ID}.supabase.co/storage/v1/object/public/{BUCKET_NAME}/{file_path}?t={timestamp}"
+        file_url = f"https://{SUPABASE_PROJECT_ID}.supabase.co/storage/v1/object/public/{BUCKET_NAME}/{selected_file}?t={timestamp}"
 
         # Create CV object with the file URL
         cv = CV(file_url)
@@ -41,23 +35,19 @@ if uploaded_file is not None:
             st.error(result["error"])
         else:
             # Display results
-            # Personal Information
             st.header("Personal Information")
             personal_info = result["personal_info"]
             st.json(personal_info)
             st.write(f"Personal Information Score: {personal_info['personal_info_score']}")
 
-            # Detected Sections
             st.header("Detected Sections")
             st.write(result["detected_sections"])
             st.write(f"Section Detection Score: {result['section_detection_score']}")
 
-            # Spelling and Grammar
             st.header("Spelling and Grammar")
             st.write(f"Error Percentage: {result['spelling_grammar_error_percentage']:.2f}%")
             st.write(f"Spelling and Grammar Score: {result['spelling_grammar_score']}")
 
-            # Content Quality Analysis
             st.header("Content Quality Analysis")
             for section, evaluation in result['content_analysis'].items():
                 st.subheader(section.capitalize())
@@ -65,7 +55,6 @@ if uploaded_file is not None:
 
             st.write(f"Overall Content Quality Score: {result['overall_score']:.2f} / 10")
 
-            # Total Score
             st.header("Total CV Score")
             total_score = (
                 personal_info['personal_info_score'] +