Update app.py

app.py

@@ -1,25 +1,38 @@
+import os, tempfile, datetime
 import gradio as gr
 import google.generativeai as genai
-import os
 from dotenv import load_dotenv
 
-# Load environment variable
+# ── 3rd‑party formatters ──────────────────────────────────────────────────────
+from docx import Document                     # python-docx
+from reportlab.lib.pagesizes import LETTER    # reportlab
+from reportlab.pdfgen import canvas
+
+# ──  Environment & Model  ──────────────────────────────────────────────────────
 load_dotenv()
 API_KEY = os.getenv("GEMINI_API_KEY")
-
 genai.configure(api_key=API_KEY)
-model = genai.GenerativeModel("models/gemini-1.5-pro-latest")
-
-# --- AI Functions ---
+model = genai.GenerativeModel("gemini-pro")
+
+# ──  Core AI helpers  ─────────────────────────────────────────────────────────
+def _ask_gemini(prompt: str) -> str:
+    """Utility wrapper with basic error‐handling."""
+    try:
+        rsp = model.generate_content(prompt, generation_config={"temperature": 0.7})
+        return rsp.text.strip()
+    except Exception as exc:
+        return f"[Gemini Error] {exc}"
+
+# ──  Business Logic  ──────────────────────────────────────────────────────────
 def generate_resume(name, email, phone, summary, experience, education, skills):
     prompt = f"""
-    Create a professional resume using the following details:
+    Create a professionally formatted resume. Use Markdown headings and no first‑person pronouns.
 
     Name: {name}
     Email: {email}
     Phone: {phone}
 
-    Summary:
+    Professional Summary:
     {summary}
 
     Experience:
@@ -30,74 +43,138 @@ def generate_resume(name, email, phone, summary, experience, education, skills):
 
     Skills:
     {skills}
-
-    Format it clearly and professionally.
     """
-    response = model.generate_content(prompt)
-    return response.text
+    return _ask_gemini(prompt)
 
 def score_resume(resume, job_desc):
     prompt = f"""
-    Evaluate how well the following resume matches the job description below. 
-    Return a match score out of 100 and specific, actionable suggestions to improve the resume.
+    Evaluate the RESUME against the JOB DESCRIPTION. Return JSON:
+    {{
+      "score": <0‑100>,
+      "suggestions": [ "…", "…" ]
+    }}
+    RESUME:
+    {resume}
+
+    JOB DESCRIPTION:
+    {job_desc}
+    """
+    return _ask_gemini(prompt)
 
-    Resume:
+def refine_text(section_text, instruction):
+    prompt = f"Refine the following text.\nInstruction: {instruction}\nText:\n{section_text}"
+    return _ask_gemini(prompt)
+
+def generate_cover_letter(resume, job_desc, tone):
+    prompt = f"""
+    Act as a career coach. Draft a one‑page cover letter **in {tone} tone** that aligns this RESUME
+    to the JOB DESCRIPTION. Do not exceed 300 words.
+
+    RESUME:
     {resume}
 
-    Job Description:
+    JOB DESCRIPTION:
     {job_desc}
     """
-    response = model.generate_content(prompt)
-    return response.text
-
-def refine_section(section_text, instruction):
-    prompt = f"Refine this section according to the instruction.\nInstruction: {instruction}\nText: {section_text}"
-    response = model.generate_content(prompt)
-    return response.text
-
-# --- Gradio UI ---
-with gr.Blocks() as demo:
-    gr.Markdown("## 🤖 AI Resume Builder using Gemini")
-
-    with gr.Tab("1️⃣ Generate Resume"):
-        name = gr.Textbox(label="Name")
-        email = gr.Textbox(label="Email")
-        phone = gr.Textbox(label="Phone")
-        summary = gr.Textbox(label="Professional Summary")
-        experience = gr.Textbox(label="Experience")
+    return _ask_gemini(prompt)
+
+# ──  File‑export helpers  ──────────────────────────────────────────────────────
+def _to_docx(text: str) -> str:
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".docx")
+    doc = Document()
+    for line in text.splitlines():
+        doc.add_paragraph(line)
+    doc.save(tmp.name)
+    return tmp.name
+
+def _to_pdf(text: str) -> str:
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
+    c = canvas.Canvas(tmp.name, pagesize=LETTER)
+    width, height = LETTER
+    y = height - 72
+    for line in text.splitlines():
+        c.drawString(72, y, line)
+        y -= 14
+        if y < 72:
+            c.showPage()
+            y = height - 72
+    c.save()
+    return tmp.name
+
+def generate_resume_and_files(*inputs):
+    resume_md = generate_resume(*inputs)
+    return (
+        resume_md,
+        _to_docx(resume_md),
+        _to_pdf(resume_md),
+    )
+
+# ──  Gradio UI  ───────────────────────────────────────────────────────────────
+with gr.Blocks(title="AI Resume Builder using Gemini") as demo:
+    gr.Markdown("## 🧠 AI Resume Builder × Gemini")
+
+    # ── Tab 1: Resume generation ─────────────────────────────────────────────
+    with gr.Tab("📄 Generate Resume"):
+        name      = gr.Textbox(label="Name")
+        email     = gr.Textbox(label="Email")
+        phone     = gr.Textbox(label="Phone")
+        summary   = gr.Textbox(label="Professional Summary")
+        experience= gr.Textbox(label="Experience")
         education = gr.Textbox(label="Education")
-        skills = gr.Textbox(label="Skills")
-        resume_output = gr.Textbox(label="Generated Resume", lines=15)
-        generate_btn = gr.Button("Generate Resume")
+        skills    = gr.Textbox(label="Skills")
+
+        resume_out = gr.Markdown(label="Generated Resume")
+        docx_file  = gr.File(label="⬇ Word (.docx)")
+        pdf_file   = gr.File(label="⬇ PDF (.pdf)")
+        gen_btn    = gr.Button("Generate Resume")
 
-        generate_btn.click(
-            fn=generate_resume,
+        gen_btn.click(
+            generate_resume_and_files,
             inputs=[name, email, phone, summary, experience, education, skills],
-            outputs=resume_output
+            outputs=[resume_out, docx_file, pdf_file],
         )
 
-    with gr.Tab("2️⃣ Score Resume Against Job"):
-        resume_input = gr.Textbox(label="Your Resume", lines=10)
-        job_desc = gr.Textbox(label="Job Description", lines=5)
-        score_output = gr.Textbox(label="Match Score & Suggestions", lines=10)
-        score_btn = gr.Button("Evaluate")
+    # ── Tab 2: Job‑match score ───────────────────────────────────────────────
+    with gr.Tab("🧮 Score Resume Against Job"):
+        resume_in   = gr.Textbox(label="Resume (Markdown)", lines=12)
+        jd_in       = gr.Textbox(label="Job Description", lines=8)
+        score_out   = gr.Markdown(label="Score & Suggestions")
+        score_btn   = gr.Button("Evaluate")
+
+        def parse_score(rsp):
+            return rsp    # we leave JSON parsing to the user for now
 
         score_btn.click(
-            fn=score_resume,
-            inputs=[resume_input, job_desc],
-            outputs=score_output
+            lambda r, jd: parse_score(score_resume(r, jd)),
+            inputs=[resume_in, jd_in],
+            outputs=score_out,
         )
 
-    with gr.Tab("3️⃣ Refine Resume Section"):
-        section_input = gr.Textbox(label="Resume Section", lines=5)
-        instruction_input = gr.Textbox(label="Refinement Instruction")
-        refined_output = gr.Textbox(label="Refined Text", lines=5)
+    # ── Tab 3: Refine section ────────────────────────────────────────────────
+    with gr.Tab("✏️ Refine Resume Section"):
+        sec_in   = gr.Textbox(label="Section Text", lines=6)
+        instr_in = gr.Textbox(label="Instruction (e.g., make concise)")
+        refined  = gr.Textbox(label="Refined Output", lines=6)
         refine_btn = gr.Button("Refine")
 
         refine_btn.click(
-            fn=refine_section,
-            inputs=[section_input, instruction_input],
-            outputs=refined_output
+            lambda text, instr: refine_text(text, instr),
+            inputs=[sec_in, instr_in],
+            outputs=refined,
+        )
+
+    # ── Tab 4: Cover‑Letter generator ────────────────────────────────────────
+    with gr.Tab("📧 Cover‑Letter Generator"):
+        cv_resume   = gr.Textbox(label="Resume (Markdown)", lines=12)
+        cv_jd       = gr.Textbox(label="Job Description", lines=8)
+        cv_tone     = gr.Radio(["Professional", "Friendly", "Enthusiastic"], value="Professional", label="Tone")
+        cv_out      = gr.Markdown(label="Generated Cover Letter")
+        cv_btn      = gr.Button("Generate Cover Letter")
+
+        cv_btn.click(
+            lambda r, jd, tone: generate_cover_letter(r, jd, tone),
+            inputs=[cv_resume, cv_jd, cv_tone],
+            outputs=cv_out,
         )
 
-demo.launch()
+demo.launch(share=False)   # ← Spaces already exposes the public URL