Update app.py

app.py

@@ -6,7 +6,6 @@ from nltk.tokenize import word_tokenize
 from nltk.corpus import wordnet
 import requests
 from typing import Optional
-import os
 import pandas as pd
 from sqlalchemy import create_engine, Column, Integer, String, Float
 from sqlalchemy.ext.declarative import declarative_base
@@ -107,11 +106,10 @@ def get_docparser_data(file, api_key, parser_id) -> Optional[dict]:
         st.error(f"Error fetching data from Docparser: {e}")
     return None
 
-def get_openai_data(file_path: str, openai_key: str) -> Optional[dict]:
+def get_openai_data(file, openai_key: str) -> Optional[dict]:
     openai.api_key = openai_key
     try:
-        with open(file_path, 'rb') as file:
-            file_content = file.read()
+        file_content = file.read()
         response = openai.Completion.create(
             engine="text-davinci-003",
             prompt=f"Extract and analyze the resume content: {file_content}",
@@ -213,31 +211,23 @@ def process_resume(file, job_description, filename, parser_choice, openai_key=No
         session.rollback()
         return None
 
-def process_resumes(folder_path, job_description, parser_choice, openai_key=None, api_key=None, parser_id=None, skill_weight=0.9, certification_weight=0.05, experience_weight=0.03, education_weight=0.02, project_weight=0.1):
-    if not os.path.isdir(folder_path):
-        st.error("Invalid folder path")
-        return []
-
+def process_resumes(files, job_description, parser_choice, openai_key=None, api_key=None, parser_id=None, skill_weight=0.9, certification_weight=0.05, experience_weight=0.03, education_weight=0.02, project_weight=0.1):
     scores = []
     processed_count = 0
 
     try:
-        pdf_files = [f for f in os.listdir(folder_path) if f.lower().endswith('.pdf')]
-
-        if not pdf_files:
-            st.warning("No PDF files found in the folder")
+        if not files:
+            st.warning("No PDF files uploaded")
             return []
 
-        total_files = len(pdf_files)
+        total_files = len(files)
         progress_bar = st.progress(0)
 
-        for index, filename in enumerate(pdf_files):
-            file_path = os.path.join(folder_path, filename)
-            with open(file_path, 'rb') as file:
-                result = process_resume(file, job_description, filename, parser_choice, openai_key, api_key, parser_id, skill_weight, certification_weight, experience_weight, education_weight, project_weight)
-                if result:
-                    scores.append(result)
-                    processed_count += 1
+        for index, file in enumerate(files):
+            result = process_resume(file, job_description, file.name, parser_choice, openai_key, api_key, parser_id, skill_weight, certification_weight, experience_weight, education_weight, project_weight)
+            if result:
+                scores.append(result)
+                processed_count += 1
 
             progress = (index + 1) / total_files
             progress_bar.progress(progress)
@@ -389,7 +379,7 @@ def main():
     choice = st.sidebar.selectbox("Menu", menu)
 
     if choice == "Home":
-        analysis_type = st.selectbox("Select Analysis Type:", ["Single Resume", "Folder Upload"])
+        analysis_type = st.selectbox("Select Analysis Type:", ["Single Resume", "Multiple Resumes"])
         method_choice = st.selectbox("Select Method:", ["Use LLM", "Use Field Extraction"])
 
         openai_key = None  # Initialize openai_key
@@ -435,12 +425,12 @@ def main():
                     else:
                         st.warning("Failed to process the resume.")
 
-        elif analysis_type == "Folder Upload":
-            folder_path = st.text_input("Resume folder path:", placeholder="e.g. C:/Users/username/resumes")
+        elif analysis_type == "Multiple Resumes":
+            uploaded_files = st.file_uploader("Upload multiple resume PDF files", type="pdf", accept_multiple_files=True)
 
             if st.button("Analyze Resumes"):
-                if not folder_path:
-                    st.error("Please enter the folder path containing resumes")
+                if not uploaded_files:
+                    st.error("Please upload resume PDF files")
                     return
                 if not job_description:
                     st.error("Please enter a job description")
@@ -452,7 +442,7 @@ def main():
                     st.error("Please enter the Docparser API key and Parser ID")
                     return
                 with st.spinner("Processing resumes..."):
-                    scores = process_resumes(folder_path, job_description, parser_choice, openai_key, api_key, parser_id, skill_weight, certification_weight, experience_weight, education_weight, project_weight)
+                    scores = process_resumes(uploaded_files, job_description, parser_choice, openai_key, api_key, parser_id, skill_weight, certification_weight, experience_weight, education_weight, project_weight)
                     if scores:
                         st.success("Analysis complete!")
                         for result in scores:
@@ -462,11 +452,11 @@ def main():
 
         with st.expander("ℹ️ How to use"):
             st.markdown("""
-            1. Select the analysis type: Single Resume or Folder Upload.
+            1. Select the analysis type: Single Resume or Multiple Resumes.
             2. Choose the method: Use LLM or Use Field Extraction.
             3. If using LLM, enter the OpenAI API key.
             4. If using Field Extraction, enter the Docparser API key and Parser ID.
-            5. Upload a resume PDF file or enter the path to a folder containing resumes.
+            5. Upload a resume PDF file or multiple files.
             6. Paste the job description.
             7. Configure the weights for skills, certifications, experience, education, and projects.
             8. Click 'Analyze' to start processing.