Create generator.py

utils/generator.py

@@ -0,0 +1,171 @@
+"""
+generator.py — AI Resume Improvement Generator.
+ 
+Uses google/flan-t5-base (free, open-source) to generate:
+  1. An improved version of the resume text
+  2. A polished professional summary
+ 
+FLAN-T5 is instruction-tuned, making it ideal for text rewriting tasks
+without requiring fine-tuning.
+ 
+Model size: ~250 MB. Downloaded once and cached automatically by
+HuggingFace transformers.
+"""
+ 
+# ---------------------------------------------------------------------------
+# Lazy model loading — keeps Streamlit startup fast
+# ---------------------------------------------------------------------------
+_pipeline = None
+ 
+ 
+def _get_pipeline():
+    """Load and cache the FLAN-T5 text2text pipeline."""
+    global _pipeline
+    if _pipeline is None:
+        from transformers import pipeline
+        _pipeline = pipeline(
+            "text2text-generation",
+            model="google/flan-t5-base",
+            max_new_tokens=512,
+        )
+    return _pipeline
+ 
+ 
+# ---------------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------------
+ 
+def generate_improved_resume(
+    resume_text: str,
+    job_description: str = "",
+    missing_sections: list = None,
+) -> str:
+    """
+    Generate an improved, ATS-friendly version of the resume.
+ 
+    Rewrites the resume with:
+      - Professional tone
+      - Bullet-point format
+      - Stronger action verbs
+      - Explicit mention of any missing sections
+ 
+    Args:
+        resume_text     : original extracted resume text
+        job_description : optional job description to tailor the rewrite
+        missing_sections: list of sections the model should add
+ 
+    Returns:
+        Improved resume text as a string.
+    """
+    if not resume_text:
+        return "No resume text provided for improvement."
+ 
+    if missing_sections is None:
+        missing_sections = []
+ 
+    pipe = _get_pipeline()
+ 
+    # Build a focused, instruction-style prompt
+    # FLAN-T5 works best with clear task instructions
+    truncated_resume = _truncate(resume_text, 400)
+    jd_hint = f"\nThe target job requires: {_truncate(job_description, 100)}" if job_description else ""
+    missing_hint = (
+        f"\nMake sure to include sections for: {', '.join(missing_sections)}."
+        if missing_sections
+        else ""
+    )
+ 
+    prompt = (
+        f"Rewrite this resume to be professional, ATS-friendly, and impactful. "
+        f"Use bullet points, strong action verbs, and quantify achievements. "
+        f"Keep all original facts.{jd_hint}{missing_hint}\n\n"
+        f"Original Resume:\n{truncated_resume}\n\n"
+        f"Improved Resume:"
+    )
+ 
+    try:
+        result = pipe(prompt, max_new_tokens=512, do_sample=False)
+        improved = result[0]["generated_text"].strip()
+        return improved if improved else "⚠️ Could not generate improvement. Please try again."
+    except Exception as e:
+        return f"⚠️ Generation error: {str(e)}"
+ 
+ 
+def generate_professional_summary(
+    name: str,
+    skills: list,
+    experience_present: bool,
+    job_title: str = "",
+) -> str:
+    """
+    Generate a short professional summary / objective paragraph.
+ 
+    Args:
+        name              : candidate name (or empty string)
+        skills            : list of extracted skill strings
+        experience_present: whether work experience was detected
+        job_title         : optional target job title
+ 
+    Returns:
+        A 2–3 sentence professional summary.
+    """
+    pipe = _get_pipeline()
+ 
+    skills_str = ", ".join(skills[:8]) if skills else "various technical skills"
+    exp_phrase = (
+        "with hands-on professional experience"
+        if experience_present
+        else "seeking to start a professional career"
+    )
+    target = f" targeting a {job_title} role" if job_title else ""
+ 
+    prompt = (
+        f"Write a 2-3 sentence professional resume summary for a candidate "
+        f"{exp_phrase} in {skills_str}{target}. "
+        f"Keep it concise, first-person, and ATS-friendly."
+    )
+ 
+    try:
+        result = pipe(prompt, max_new_tokens=120, do_sample=False)
+        return result[0]["generated_text"].strip()
+    except Exception as e:
+        return f"⚠️ Summary generation error: {str(e)}"
+ 
+ 
+def generate_section_content(section_name: str, context: str) -> str:
+    """
+    Generate content for a specific missing resume section.
+ 
+    Args:
+        section_name: e.g., 'Projects', 'Skills', 'Experience'
+        context     : relevant resume text to base the generation on
+ 
+    Returns:
+        Generated section content as a string.
+    """
+    pipe = _get_pipeline()
+ 
+    prompt = (
+        f"Based on the following resume context, write a professional "
+        f"'{section_name}' section in bullet-point format:\n\n"
+        f"{_truncate(context, 200)}\n\n"
+        f"{section_name} Section:"
+    )
+ 
+    try:
+        result = pipe(prompt, max_new_tokens=200, do_sample=False)
+        return result[0]["generated_text"].strip()
+    except Exception as e:
+        return f"⚠️ Section generation error: {str(e)}"
+ 
+ 
+# ---------------------------------------------------------------------------
+# Internal helpers
+# ---------------------------------------------------------------------------
+ 
+def _truncate(text: str, max_words: int) -> str:
+    """Truncate text to max_words words to stay within model context limit."""
+    words = text.split()
+    if len(words) > max_words:
+        return " ".join(words[:max_words]) + "..."
+    return text