Update app.py

app.py

@@ -1,31 +1,96 @@
-from transformers import pipeline
+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()
 
-# Load model (demo sentiment classifier — replace with your own model later)
+# 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")
 
-# Define function to process input and return score
-def match_profile(resume_text, linkedin_text):
+# 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:
-        input_text = (resume_text + " " + linkedin_text)[:1000]  # limit length
-        result = classifier(input_text)
-        label = result[0]['label']
-        score = result[0]['score'] * 100
-        return f"Predicted Label: {label}\nSuitability Score: {score:.2f}"
+        # 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)}"
 
-# Define Gradio interface
-interface = gr.Interface(
-    fn=match_profile,
+# Gradio UI
+gr.Interface(
+    fn=process_resume,
     inputs=[
-        gr.Textbox(lines=10, placeholder="Paste Resume Text here...", label="Resume Text"),
-        gr.Textbox(lines=5, placeholder="Paste LinkedIn Profile Summary here...", label="LinkedIn Text")
+        gr.File(label="Upload Resume (PDF)", file_types=[".pdf"]),
+        gr.Textbox(label="Salesforce Record ID (Agent_Prospect__c)")
     ],
     outputs="text",
-    title="LIC Profile Matcher",
-    description="This tool matches an agent’s resume and LinkedIn profile using a BERT model and returns a suitability score."
-)
-
-# Launch WITHOUT the share button
-interface.launch(share=False)  # 👈 No Share via Link button
+    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)