Delete app.py

app.py

@@ -1,84 +0,0 @@
-import streamlit as st
-import os
-from cv_analyzer import analyze_cv
-from cv_quality import CV
-from get_supabase import Supabase
-from datetime import datetime
-
-st.set_page_config(page_title="CV Analyzer", layout="wide")
-st.title('CV Analyzer')
-
-# Initialize Supabase client
-supabase_client = Supabase().init_supabase_client()
-
-# Supabase storage details
-BUCKET_NAME = "CVs UX"
-SUPABASE_PROJECT_ID = "abjtqzgnrtsikkqgnqeg"
-
-uploaded_file = st.file_uploader("Choose a CV file", type=['pdf', 'docx', 'txt'])
-
-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"}
-            )
-
-        # Construct the public URL of the uploaded 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}"
-
-        # Create CV object with the file URL
-        cv = CV(file_url)
-        result = cv.analyse_cv_quality()
-
-        if "error" in result:
-            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())
-                st.json(evaluation)
-
-            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'] +
-                result['section_detection_score'] +
-                result['spelling_grammar_score'] +
-                result['overall_score']
-            )
-            st.write(f"Total Score: {total_score:.2f}")
-
-if __name__ == "__main__":
-    st.sidebar.title("About")
-    st.sidebar.info(
-        "This CV Analyzer extracts personal information, detects sections, "
-        "checks spelling and grammar, analyzes content quality, "
-        "and provides a detailed evaluation of the CV."
-    )