Update app.py

app.py

@@ -1,47 +1,85 @@
 import gradio as gr
 import base64
-from jinja2 import Template
+import io
+from reportlab.lib.pagesizes import A4
+from reportlab.pdfgen import canvas
+from reportlab.lib.utils import ImageReader
 from transformers import pipeline
-from weasyprint import HTML
 
-# Load template
-with open("template.html", "r", encoding="utf-8") as f:
-    template_html = f.read()
+# Load FLAN-T5 model from Hugging Face
+summary_generator = pipeline("text2text-generation", model="google/flan-t5-base")
 
-# Load model for summary generation
-summary_gen = pipeline("text2text-generation", model="google/flan-t5-base")
+def generate_resume(name, job_title, email, phone, skills, education, experience, photo):
+    # Generate a passionate summary
+    prompt = f"Write a passionate summary for a person named {name}, applying as a {job_title}, skilled in {skills}, with experience in {experience}."
+    summary = summary_generator(prompt, max_length=200)[0]["generated_text"]
+
+    # Create PDF buffer
+    buffer = io.BytesIO()
+    pdf = canvas.Canvas(buffer, pagesize=A4)
+    width, height = A4
+    y = height - 50
+
+    pdf.setFont("Helvetica-Bold", 20)
+    pdf.drawString(50, y, name)
+
+    y -= 25
+    pdf.setFont("Helvetica", 14)
+    pdf.drawString(50, y, job_title)
+
+    y -= 20
+    pdf.setFont("Helvetica", 12)
+    pdf.drawString(50, y, f"Email: {email}")
+    y -= 20
+    pdf.drawString(50, y, f"Phone: {phone}")
 
-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")
-
-    # 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"]
-
-    # 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"
+        image = ImageReader(photo)
+        pdf.drawImage(image, width - 130, height - 130, width=80, height=80, mask='auto')
+
+    y -= 40
+    pdf.setFont("Helvetica-Bold", 16)
+    pdf.drawString(50, y, "Professional Summary")
+    y -= 20
+    pdf.setFont("Helvetica", 12)
+    for line in summary.split(". "):
+        pdf.drawString(50, y, f"- {line.strip()}")
+        y -= 18
+
+    y -= 20
+    pdf.setFont("Helvetica-Bold", 16)
+    pdf.drawString(50, y, "Skills")
+    y -= 20
+    pdf.setFont("Helvetica", 12)
+    for skill in skills.split(","):
+        pdf.drawString(50, y, f"- {skill.strip()}")
+        y -= 18
+
+    y -= 20
+    pdf.setFont("Helvetica-Bold", 16)
+    pdf.drawString(50, y, "Education")
+    y -= 20
+    pdf.setFont("Helvetica", 12)
+    for edu in education.split(";"):
+        pdf.drawString(50, y, f"- {edu.strip()}")
+        y -= 18
+
+    y -= 20
+    pdf.setFont("Helvetica-Bold", 16)
+    pdf.drawString(50, y, "Experience")
+    y -= 20
+    pdf.setFont("Helvetica", 12)
+    for exp in experience.split(";"):
+        pdf.drawString(50, y, f"- {exp.strip()}")
+        y -= 18
+
+    pdf.save()
+    buffer.seek(0)
+    return buffer
 
 # Gradio UI
 iface = gr.Interface(
-    fn=build_resume,
+    fn=generate_resume,
     inputs=[
         gr.Text(label="Full Name"),
         gr.Text(label="Job Title"),
@@ -50,11 +88,11 @@ iface = gr.Interface(
         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"),
+        gr.File(label="Profile Photo (optional)", type="binary")
     ],
     outputs=gr.File(label="📄 Download Resume PDF"),
-    title="AI Resume Builder",
-    description="Build a beautiful resume with auto-generated professional summary using Hugging Face FLAN-T5."
+    title="🧠 AI Resume Builder (ReportLab + FLAN-T5)",
+    description="Generates a beautiful resume with AI-generated summary using FLAN-T5 and exports a working PDF (100% compatible with Hugging Face Spaces)."
 )
 
 iface.launch()