Update app.py

app.py

@@ -1,19 +1,25 @@
 import spacy
 import streamlit as st
+import subprocess
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 import PyPDF2
 import nltk
 from nltk.corpus import stopwords
 from nltk.tokenize import word_tokenize
-from gemini_flash import GeminiFlash  # Assuming Gemini Flash is installed
+from gemini_flash import GeminiFlash  # Adjust if Gemini Flash is available
 
 # Ensure that NLTK's stopwords are available
 nltk.download('punkt')
 nltk.download('stopwords')
 
-# Load spaCy model for NER
-nlp = spacy.load("en_core_web_sm")
+# Check if the spaCy model is already installed, if not, install it
+try:
+    nlp = spacy.load("en_core_web_sm")
+except OSError:
+    # Install the model if it's not available
+    subprocess.run(["python", "-m", "spacy", "download", "en_core_web_sm"], check=True)
+    nlp = spacy.load("en_core_web_sm")
 
 # Initialize Gemini Flash for prompt engineering
 prompt_engineer = GeminiFlash()
@@ -88,4 +94,3 @@ if resume_text and job_description:
     # For demonstration purposes, assume a function `get_llm_response` exists that interacts with a model.
     # response = get_llm_response(enhanced_prompt)
     # st.write("LLM Response:", response)
-