Update app.py

app.py

@@ -1,54 +1,60 @@
 import gradio as gr
 import base64
-import pdfkit
 from jinja2 import Template
+from transformers import pipeline
+from weasyprint import HTML
 
-# Load HTML template
+# Load template
 with open("template.html", "r", encoding="utf-8") as f:
     template_html = f.read()
 
-def generate_resume(name, job_title, email, phone, summary, skills, education, experience, photo):
-    photo_base64 = ""
-    if photo is not None:
-        photo_base64 = base64.b64encode(photo.read()).decode('utf-8')
-
-    data = {
-        "name": name,
-        "job_title": job_title,
-        "email": email,
-        "phone": phone,
-        "summary": summary,
-        "skills": [s.strip() for s in skills.split(",")],
-        "education": [e.strip() for e in education.split(";")],
-        "experience": [x.strip() for x in experience.split(";")],
-        "photo_base64": photo_base64
-    }
+# Load model for summary generation
+summary_gen = pipeline("text2text-generation", model="google/flan-t5-base")
 
-    template = Template(template_html)
-    rendered_html = template.render(**data)
+def build_resume(name, job_title, email, phone, skills, education, experience, photo):
+    # Convert photo to base64
+    photo_b64 = ""
+    if photo:
+        photo_b64 = base64.b64encode(photo.read()).decode("utf-8")
 
-    with open("resume_output.html", "w", encoding="utf-8") as f:
-        f.write(rendered_html)
+    # AI-generated summary
+    prompt = f"Write a professional and passionate summary for {name}, applying as a {job_title}, with skills {skills}, and experience in {experience}."
+    summary = summary_gen(prompt, max_length=200)[0]["generated_text"]
 
-    pdfkit.from_file("resume_output.html", "resume.pdf")
+    # Prepare template data
+    template = Template(template_html)
+    html_resume = template.render(
+        name=name,
+        job_title=job_title,
+        email=email,
+        phone=phone,
+        summary=summary,
+        skills=[s.strip() for s in skills.split(",")],
+        education=[e.strip() for e in education.split(";")],
+        experience=[e.strip() for e in experience.split(";")],
+        photo_base64=photo_b64
+    )
+
+    # Generate PDF
+    HTML(string=html_resume).write_pdf("resume.pdf")
     return "resume.pdf"
 
-inputs = [
-    gr.Text(label="Full Name"),
-    gr.Text(label="Job Title"),
-    gr.Text(label="Email"),
-    gr.Text(label="Phone"),
-    gr.Textbox(label="Professional Summary", lines=4),
-    gr.Textbox(label="Skills (comma-separated)"),
-    gr.Textbox(label="Education (separate with ; )"),
-    gr.Textbox(label="Experience (separate with ; )"),
-    gr.File(label="Upload Profile Photo (Optional)", type="binary")
-]
-
-gr.Interface(
-    fn=generate_resume,
-    inputs=inputs,
+# Gradio UI
+iface = gr.Interface(
+    fn=build_resume,
+    inputs=[
+        gr.Text(label="Full Name"),
+        gr.Text(label="Job Title"),
+        gr.Text(label="Email"),
+        gr.Text(label="Phone"),
+        gr.Textbox(label="Skills (comma-separated)"),
+        gr.Textbox(label="Education (use ; to separate items)"),
+        gr.Textbox(label="Experience (use ; to separate items)"),
+        gr.File(label="Profile Photo (optional)", type="binary"),
+    ],
     outputs=gr.File(label="📄 Download Resume PDF"),
-    title="🧠 AI Resume Builder",
-    description="Enter your details and generate a polished PDF resume instantly."
-).launch()
+    title="AI Resume Builder",
+    description="Build a beautiful resume with auto-generated professional summary using Hugging Face FLAN-T5."
+)
+
+iface.launch()