Update app.py

app.py

@@ -1,12 +1,12 @@
 import gradio as gr
 import requests
-import json
 import os
+import csv
 from sentence_transformers import util
 
 # Set up API endpoint and API Key
 api_key = os.getenv("GOOGLE_API_KEY")  # Store your API Key in environment variables
-api_url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1-2:embedText"  # Adjust as per your model version
+api_url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-1-5-flash:embedText"  # Updated for Gemini 1.5 Flash model
 
 headers = {
     "Authorization": f"Bearer {api_key}",
@@ -15,7 +15,7 @@ headers = {
 
 def get_gemini_embeddings(text):
     data = {
-        "model": "gemini-1-2",  # Replace with the actual model you are using
+        "model": "gemini-1-5-flash",  # Use the Gemini 1.5 Flash model
         "text": text
     }
 
@@ -30,7 +30,8 @@ def get_gemini_embeddings(text):
         return []
 
 def extract_text_from_resume(resume_file):
-    # Extract text from resume (you can use libraries like PyPDF2 or textract for PDFs)
+    # Extract text from resume (for example, using PyPDF2 or textract for PDFs)
+    # This placeholder should be replaced with actual code for resume text extraction
     return "Sample resume text"
 
 def extract_leadership_experience(resume_text):
@@ -42,7 +43,6 @@ def extract_entities_via_gemini(resume_text):
     return {"name": "John Doe", "email": "john.doe@example.com", "contact": "123-456-7890"}
 
 def save_results_to_csv(results):
-    import csv
     csv_file_path = "/tmp/results.csv"
     with open(csv_file_path, mode='w', newline='') as file:
         writer = csv.writer(file)
@@ -54,11 +54,11 @@ def save_results_to_csv(results):
 def check_similarity(job_description, resume_files):
     results = []
     
-    # Get embeddings for the job description using Gemini
+    # Get embeddings for the job description using Gemini 1.5 Flash
     job_emb = get_gemini_embeddings(job_description)
     
     if not job_emb:
-        return "Error in embedding job description using Gemini API."
+        return "Error in embedding job description using Gemini 1.5 Flash API."
     
     for resume_file in resume_files:
         resume_text = extract_text_from_resume(resume_file)
@@ -66,7 +66,7 @@ def check_similarity(job_description, resume_files):
             results.append((resume_file.name, 0, "Not Eligible", None, "No leadership experience", "No Email", "No Contact"))
             continue
         
-        # Get embeddings for the resume using Gemini
+        # Get embeddings for the resume using Gemini 1.5 Flash
         resume_emb = get_gemini_embeddings(resume_text)
         
         if not resume_emb: