@feat : enhance cover letter generation with markdown formatting; update PDF saving logic

app/api/routes.py

@@ -6,11 +6,7 @@ from app.services.resume_parser import extract_resume_text
 from app.utils.file_utils import generate_unique_filename
 from fastapi import UploadFile, File
 from app.services.hf_storage_service import HuggingFaceStorageService
-import os
-from dotenv import load_dotenv
-
-
-
+from app.services.pdf_creator import build_cover_letter_md, convert_md_to_text
 
 storage_service = HuggingFaceStorageService()
 
@@ -20,13 +16,26 @@ router = APIRouter()
 async def generate_cover_letter_api(data: GenerateRequest):
     try:
         if len(data.job_details) > 2048:
-            raise HTTPException(status_code=400, detail="Job details are too long")
+            raise HTTPException(status_code=400, detail="Job detail is too long")
 
         resume_text = extract_resume_text(data.resume_path)
         letter_text = await generate_cover_letter(data, resume_text)
+        
+        md_cover_letter = build_cover_letter_md(
+            your_name=data.full_name,
+            postal_code=data.postal_code,
+            city=data.city,
+            email=data.email,
+            phone=data.phone_number,
+            job_title=data.job_title,
+            company_name=data.company_name,
+            generated_paragraphs=letter_text
+        )
+
+        text_cover_letter = convert_md_to_text(md_cover_letter)
 
         filename = generate_unique_filename()
-        pdf_path = save_pdf(letter_text, filename)
+        pdf_path = save_pdf(text_cover_letter, filename)
 
         return GenerateResponse(
             letter=letter_text,

app/models/schema.py

@@ -3,6 +3,14 @@ from pydantic import BaseModel
 class GenerateRequest(BaseModel):
     job_details: str
     resume_path: str
+    job_title: str
+    company_name: str
+    email: str
+    phone_number: str
+    postal_code: str
+    city: str
+    country: str
+    full_name: str
 
 class GenerateResponse(BaseModel):
     letter: str

app/services/generator.py

@@ -29,35 +29,30 @@ async def generate_cover_letter(data: GenerateRequest, resume_text: str) -> str:
 
         altered_job_details = await job_details_alteration(data.job_details)
 
+                
         prompt = f"""
-        Please generate a professional cover letter in **markdown format** based on the following information:
-
-        **Job Details:**
-        {altered_job_details}
-
-        **Resume Content:**
-        {resume_text}
-
-
-        **Important:** 
-        - Format the output as proper markdown
-        - Use appropriate headers (# ## ###)
-        - Use **bold** for emphasis where appropriate
-        - Use bullet points or numbered lists where relevant
-        - Include proper spacing and line breaks
-        - Start with the applicant's contact information as a header
-        - Include date and employer information
-        - Structure it as a professional business letter in markdown format
-
-        Please ensure the cover letter is:
-        1. Tailored specifically to the job requirements
-        2. Highlights relevant experience from the resume
-        3. Professional and engaging tone
-        4. Proper markdown formatting throughout
-        """
+           You are a professional cover letter writer.  
+
+            Generate exactly **3 paragraphs** of a cover letter in **markdown format** based on the following:  
+
+            **Job Title:** {data.job_title}  
+            **Company Name:** {data.company_name}  
+            **Job Details:** {altered_job_details}  
+            **Resume Content:** {resume_text}  
+
+            ### Instructions:
+            - Write in a professional and engaging tone.  
+            - Tailor the content specifically to the job requirements.  
+            - Highlight relevant experience, skills, and achievements from the resume.  
+            - Each paragraph should be distinct and flow logically:  
+            1. Introduction (state interest and motivation)  
+            2. Main body (highlight relevant skills and experience)  
+            3. Closing (express enthusiasm and call to action)  
+            - Return only **3 paragraphs**, no extra text.  
+            - Do not include contact details, date, or subject line (these will be injected into a template).
+            """
         
         messages = [
-        {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful AI cover letter bot that generates professional cover letters in markdown format. Always respond with properly formatted markdown."},
         {"role": "user", "content": prompt}
         ]
         # Apply chat template
@@ -123,7 +118,6 @@ async def job_details_alteration(job_details:str) -> str:
     - If a section is not found, skip it. """
 
         messages = [
-            {"role":"system", "content": "You are a job description cleaner. I will give you a long job description that includes many sections like company intro, perks, and marketing fluff. "},
             {"role": "user", "content": prompt}
         ]
         text = tokenizer.apply_chat_template(

app/services/pdf_creator.py

@@ -4,6 +4,7 @@ from datetime import datetime
 import io
 from app.services.hf_storage_service import HuggingFaceStorageService
 import unicodedata
+import markdown2
 
 storage_service = HuggingFaceStorageService()
 
@@ -54,3 +55,34 @@ def save_pdf(text: str, filename: str) -> str:
     # Upload PDF bytes to Hugging Face using your class method
     url = storage_service.upload_file_to_hf(file_content=pdf_data, folder="cover-letters", filename=filename)
     return url
+
+def build_cover_letter_md(
+    your_name, postal_code, city, email, phone,
+    job_title, company_name, generated_paragraphs
+):
+    template = f"""
+# {your_name}  
+
+{postal_code} {city}  
+{email} | {phone}  
+
+---
+
+**Subject:** {job_title}  
+
+Dear {company_name} Team,  
+
+{generated_paragraphs}  
+
+Yours sincerely,  
+{your_name}
+"""
+    return template
+
+
+def convert_md_to_text(md_text: str) -> str:
+    """Convert markdown to plain text for PDF saving."""
+    # markdown2 converts to HTML, then strip tags
+    html = markdown2.markdown(md_text)
+    plain_text = "".join(html.split("<")[0] if "<" in html else html for html in html.split(">"))
+    return plain_text