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import os
import pdfplumber
import gradio as gr
from transformers import pipeline
from simple_salesforce import Salesforce
from dotenv import load_dotenv
import requests
import base64
# Load environment variables
load_dotenv()
# Salesforce credentials from .env
SF_USERNAME = os.getenv("SF_USERNAME")
SF_PASSWORD = os.getenv("SF_PASSWORD")
SF_SECURITY_TOKEN = os.getenv("SF_SECURITY_TOKEN")
SF_LOGIN_URL = os.getenv("SF_LOGIN_URL")
SF_OBJECT_NAME = os.getenv("SF_OBJECT_NAME", "Agent_Prospect__c")
SF_SCORE_FIELD = os.getenv("SF_SCORE_FIELD", "Suitability_Score__c")
SF_LINK_FIELD = os.getenv("SF_RESUME_FIELD_LINK", "Resume_File_Link__c")
SF_RECORD_ID = os.getenv("SF_RECORD_ID")
# Hugging Face classifier
classifier = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
# Connect to Salesforce
sf = Salesforce(
username=SF_USERNAME,
password=SF_PASSWORD,
security_token=SF_SECURITY_TOKEN,
domain="login" if "login" in SF_LOGIN_URL else "test"
)
def process_resume(file, record_id):
try:
# Extract text from PDF
with pdfplumber.open(file.name) as pdf:
text = ""
for page in pdf.pages:
text += page.extract_text() or ""
if not text.strip():
return "❌ Could not extract text from PDF."
# Call HF model
result = classifier(text[:1000])
score_text = result[0]['label']
score_val = float(result[0]['score']) * 100
summary = f"Predicted Label: {score_text}\nSuitability Score: {score_val:.2f}"
# Convert PDF to base64
with open(file.name, "rb") as f:
pdf_content = f.read()
pdf_base64 = base64.b64encode(pdf_content).decode("utf-8")
# Upload PDF to Salesforce using ContentVersion
content_result = sf.ContentVersion.create({
"Title": "Resume",
"PathOnClient": file.name,
"VersionData": pdf_base64
})
content_doc_id = content_result.get("id")
# Link file to record
sf.ContentDocumentLink.create({
"ContentDocumentId": content_doc_id,
"LinkedEntityId": record_id,
"ShareType": "V",
"Visibility": "AllUsers"
})
# Build download link (you can improve this with actual org domain)
download_link = f"https://{sf.sf_instance}/sfc/servlet.shepherd/document/download/{content_doc_id}"
# Update score + file link on record
sf.__getattr__(SF_OBJECT_NAME).update(record_id, {
SF_SCORE_FIELD: round(score_val, 2),
SF_LINK_FIELD: download_link
})
return f"{summary}\n\n✅ Score saved in Salesforce.\n📎 PDF uploaded: [Download]({download_link})"
except Exception as e:
return f"❌ Error: {str(e)}"
# Gradio UI
gr.Interface(
fn=process_resume,
inputs=[
gr.File(label="Upload Resume (PDF)", file_types=[".pdf"]),
gr.Textbox(label="Salesforce Record ID (Agent_Prospect__c)")
],
outputs="text",
title="LIC Resume AI Scorer",
description="Upload a PDF resume and enter Salesforce Record ID to auto-score and attach the file."
).launch(share=False) |