Create app.py

app.py

@@ -0,0 +1,71 @@
+import gradio as gr
+import pandas as pd
+import traceback
+from model import screen_resumes_backend
+
+
+def process(job_description, files, model_option, threshold):
+    try:
+        if not files:
+            return "❌ Upload CV files first", None, None
+
+        if not job_description or len(job_description.strip()) < 20:
+            return "❌ Job description too short", None, None
+
+        results, summary, _, zip_path = screen_resumes_backend(
+            job_description,
+            files,
+            model_option,
+            threshold
+        )
+
+        df = pd.DataFrame(results) if results else None
+
+        return summary, df, zip_path
+
+    except Exception as e:
+        return f"❌ Error: {str(e)}\n{traceback.format_exc()}", None, None
+
+
+# ================= UI =================
+with gr.Blocks(title="AI Resume Screener") as demo:
+
+    gr.HTML("""
+    <h1 style="text-align:center; color:#2563eb;">
+    🧠 AI Resume Screening System
+    </h1>
+    """)
+
+    job_desc = gr.Textbox(
+        label="Job Description",
+        lines=8,
+        placeholder="Enter job requirements..."
+    )
+
+    model_choice = gr.Dropdown(
+        ["Fast (MiniLM)", "Balanced (Recommended)", "High Accuracy"],
+        value="Balanced (Recommended)",
+        label="Model"
+    )
+
+    threshold = gr.Slider(0.4, 0.9, value=0.6, step=0.05, label="Threshold")
+
+    file_input = gr.File(
+        label="Upload CVs (PDF/DOCX)",
+        file_count="multiple"
+    )
+
+    btn = gr.Button("Process")
+
+    output_text = gr.Markdown()
+    output_table = gr.DataFrame()
+    download_file = gr.File()
+
+    btn.click(
+        process,
+        inputs=[job_desc, file_input, model_choice, threshold],
+        outputs=[output_text, output_table, download_file]
+    )
+
+if __name__ == "__main__":
+    demo.launch()