Update app.py

app.py

@@ -1,18 +1,32 @@
 from transformers import pipeline
-from gradio import Interface
+import gradio as gr
 
-# Load a simple text classification model
+# Load a pre-trained sentiment analysis model for demo purposes
+# You can replace this with your fine-tuned model later
 classifier = pipeline("text-classification", model="nlptown/bert-base-multilingual-uncased-sentiment")
 
 def match_profile(resume_text, linkedin_text):
+    """
+    Combines resume and LinkedIn text, sends to classifier,
+    and returns a Suitability Score (0-100).
+    """
     input_text = resume_text + " " + linkedin_text
     result = classifier(input_text)
+    label = result[0]['label']
     score = result[0]['score'] * 100
-    return f"Suitability Score: {score:.2f}"
+    return f"Predicted Label: {label}\nSuitability Score: {score:.2f}"
 
-iface = Interface(fn=match_profile,
-                  inputs=["text", "text"],
-                  outputs="text",
-                  title="LIC Profile Matcher")
+# Gradio Interface
+interface = gr.Interface(
+    fn=match_profile,
+    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")
+    ],
+    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."
+)
 
-iface.launch()
+# Launch the app
+interface.launch()