Update app.py

app.py

@@ -5,9 +5,9 @@ import spacy
 from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
 from docx import Document
 import re
-from nltk.corpus import words
 import dateparser
 from datetime import datetime
+import os
 
 # Load SpaCy model for dependency parsing
 nlp_spacy = spacy.load('en_core_web_sm')
@@ -17,13 +17,15 @@ tokenizer = AutoTokenizer.from_pretrained("Babelscape/wikineural-multilingual-ne
 model = AutoModelForTokenClassification.from_pretrained("Babelscape/wikineural-multilingual-ner")
 nlp_ner = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
 
-english_words = set(words.words())
-
 # Function to authenticate with Gemini API
-def authenticate_gemini(api_key):
+def authenticate_gemini():
+    api_key = os.environ.get("GOOGLE_GEMINI_API_KEY")
+    if not api_key:
+        st.error("Google Gemini API key not found. Please set it in the Hugging Face Spaces secrets.")
+        return None
     try:
         genai.configure(api_key=api_key)
-        model = genai.GenerativeModel(model_name="gemini-1.5-flash-latest")
+        model = genai.GenerativeModel(model_name="gemini-pro")
         st.success("Gemini API successfully configured.")
         return model
     except Exception as e:
@@ -120,19 +122,16 @@ def main():
     st.title("Comprehensive Resume Analyzer")
     st.write("Upload a resume to extract information, generate a summary, and analyze details.")
 
-    # Input for API key
-    api_key = st.text_input("Enter your Google Gemini API key", type="password")
+    # Authenticate with Gemini API
+    model = authenticate_gemini()
+    if model is None:
+        return
 
     # File uploader for resume input
     uploaded_file = st.file_uploader("Choose a PDF or DOCX file", type=["pdf", "docx", "doc"])
 
-    if uploaded_file is not None and api_key:
+    if uploaded_file is not None:
         try:
-            # Authenticate with Google Gemini API
-            model = authenticate_gemini(api_key)
-            if model is None:
-                return
-
             # Extract text from the uploaded resume
             file_ext = uploaded_file.name.split('.')[-1].lower()
             if file_ext == 'pdf':