@feat : cover letter enhanced, cl upload file, comment out some hf code.

README.md

@@ -8,3 +8,8 @@ pinned: false
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+<!-- .\venv\Scripts\activate.bat -->
+<!-- pip install -r requirements.txt -->
+<!-- uvicorn app.main:app --reload -->

app/api/routes.py

@@ -1,6 +1,6 @@
 from fastapi import APIRouter, HTTPException
 from app.models.schema import GenerateRequest, GenerateResponse
-from app.services.generator import generate_cover_letter
+from app.services.generator import coverLetterGenerativeAIBot
 from app.services.pdf_creator import save_pdf
 from app.services.resume_parser import extract_resume_text
 from app.utils.file_utils import generate_unique_filename
@@ -19,27 +19,27 @@ async def generate_cover_letter_api(data: GenerateRequest):
             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)
-        print(letter_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
-        )
+        letter_text = await coverLetterGenerativeAIBot(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)
+        # text_cover_letter = convert_md_to_text(md_cover_letter)
 
         filename = generate_unique_filename()
-        pdf_path = save_pdf(text_cover_letter, filename)
+        # pdf_path = save_pdf(text_cover_letter, filename)
 
         return GenerateResponse(
             letter=letter_text,
-            pdf_url=pdf_path
+            pdf_url=""
         )
 
     except HTTPException as http_exc:
@@ -72,4 +72,29 @@ async def upload_resume(resume: UploadFile = File(...)):
             "success": False,
             "error": str(e)
         }
-        
+     
+@router.post("/upload-file")
+async def upload_file(pdf: UploadFile = File(...)):
+    try:
+        # Read resume content
+        pdf_content = await pdf.read()
+        
+        # Upload to HuggingFace Hub
+        pdf_url = storage_service.upload_file_to_hf(
+            file_content=pdf_content,
+            folder="cover-letters",
+            filename=pdf.filename
+        )
+        
+        return {
+            "success": True,
+            "url": pdf_url
+        }
+        
+    except Exception as e:
+        return {
+            "success": False,
+            "error": str(e)
+        }
+
+

app/main.py

@@ -38,7 +38,6 @@ async def log_requests(request: Request, call_next):
 
 @app.get("/")
 def ping():
-    print()
     return {"status": "ok"}
 
 

app/services/generator.py

@@ -2,238 +2,361 @@ from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import torch
 from app.models.schema import GenerateRequest
 import os
+from openai import OpenAI
+import tiktoken
 
 
-os.environ["TRANSFORMERS_CACHE"] = "/code/backend/transformers_cache"
-os.makedirs(os.environ["TRANSFORMERS_CACHE"], exist_ok=True)
+# os.environ["TRANSFORMERS_CACHE"] = "/code/backend/transformers_cache"
+# os.makedirs(os.environ["TRANSFORMERS_CACHE"], exist_ok=True)
 
 
-# Initialization model immediately after server is Up!
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.float16 if device == "cuda" else torch.float32
+# # Initialization model immediately after server is Up!
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# dtype = torch.float16 if device == "cuda" else torch.float32
 
-# TinyLlama/TinyLlama-1.1B-Chat-v1.0
-model_name = "Qwen/Qwen2.5-0.5B-Instruct"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(
-    model_name,
-    dtype="auto",
-    device_map="auto"
-)
+# # TinyLlama/TinyLlama-1.1B-Chat-v1.0
+# model_name = "Qwen/Qwen2.5-0.5B-Instruct"
+# tokenizer = AutoTokenizer.from_pretrained(model_name)
+# model = AutoModelForCausalLM.from_pretrained(
+#     model_name,
+#     dtype="auto",
+#     device_map="auto"
+# )
 
-model = model.to(device)
+# model = model.to(device)
 
 
-async def generate_cover_letter(data: GenerateRequest, resume_text: str) -> str:
-    try:
+# async def generate_cover_letter(data: GenerateRequest, resume_text: str) -> str:
+#     try:
 
-        altered_job_details = await job_details_alteration(data.job_details)
+#         altered_job_details = await job_details_alteration(data.job_details)
 
                 
-        prompt = f"""
-            # AI Writer Bot Prompt
+#         prompt = f"""
+#             # AI Writer Bot Prompt
 
-            You are an expert  writer that creates personalized, human-written paragraphs based on job descriptions and CV information. Your goal is to craft authentic paragraphs that sound like they're written by a Pakistani professional with 6.5 IELTS band score - natural, clear, but not overly sophisticated or AI-generated.
+#             You are an expert  writer that creates personalized, human-written paragraphs based on job descriptions and CV information. Your goal is to craft authentic paragraphs that sound like they're written by a Pakistani professional with 6.5 IELTS band score - natural, clear, but not overly sophisticated or AI-generated.
 
-            ## Input Requirements
+#             ## Input Requirements
 
-            - ** Job Title :** Exact title of the position being applied for
-            - **Job Description (JD):** Complete job posting with requirements, responsibilities, and company information
-            - **CV/Resume:** Tailored resume specifically prepared for the target role
+#             - ** Job Title :** Exact title of the position being applied for
+#             - **Job Description (JD):** Complete job posting with requirements, responsibilities, and company information
+#             - **CV/Resume:** Tailored resume specifically prepared for the target role
             
             
-            Job Title:
-            {data.job_title}  
+#             Job Title:
+#             {data.job_title}  
 
-            Job Description:
-            {altered_job_details}  
+#             Job Description:
+#             {altered_job_details}  
             
-            Resume Content:
-            {resume_text} 
-
-            ## Output Format
-
-            Provide only the paragraph content in this format:
-
-            Dear {data.company_name},
-
-            ## Core Writing Instructions
-
-            ### Content Strategy (German Market Focus)
-
-            1. **Opening:** State the position you're applying for and briefly explain your interest in the company mentioned in the job details
-            2. **Body:** Focus on what value you bring to the company (not what you can gain from them)
-            3. **Skills Gap Handling:** If you lack certain required skills, show eagerness to learn and provide examples of quick skill acquisition
-            4. **Experience Mismatch:** If years of experience don't perfectly match (e.g., job asks 5+, you have ~4.5), emphasize the quality and relevance of your experience
-            5. **Closing:** Professional closing with availability and next steps
-
-            ### Language Requirements
-
-            - **Tone:** Professional and formal (German market prefers formal tone over conversational)
-            - **Length:** Concise and focused (3 paragraphs preferred for German market, maximum 4 paragraphs)
-            - **Total word count:** 250-400 words maximum
-            - **Per paragraph:** 70-100 words (3-4 sentences each)
-            - **Opening:** 50-70 words
-            - **Body:** 80-100 words  
-            - **Closing:** 50-70 words
-            - **Format:** Use continuous text only - no bullet points or lists within the paragraph body
-            - **Avoid These Overused Words:**
-            - Robust → use "strong," "reliable," or "sturdy"
-            - Keen → use "eager," "interested," or "enthusiastic"  
-            - Scalable → use "that can handle growth"
-            - Leverage → use "use"
-            - Utilize → use "use"
-            - Innovative → describe specific innovation
-            - Optimal → use "best," "ideal," or "most effective"
-            - Capability → use "ability," "feature," or "function"
-            - Interface → use "connect," "work with," or describe interaction
-            - Efficient → use "quick," "streamlined," or describe time savings
-            - Paradigm → use "model," "approach," or "way of thinking"
-            - Facilitate → use "help," "make easier," or "support"
-            - Optimize → use "improve," "refine," or describe enhancement
-
-            ### Specific Content Elements to Include When Relevant
+#             Resume Content:
+#             {resume_text} 
 
-            #### German Language Skills
+#             ## Output Format
 
-            - Mention motivation for learning German and timeline goals
-            - Address language barrier concerns proactively
+#             Provide only the paragraph content in this format:
 
-            #### Technology Learning Appetite
+#             Dear {data.company_name},
 
-            - Show willingness to learn new technologies (e.g., SAP, Vue.js, Ai, LLMs)
-            - Highlight strong fundamentals that enable quick learning
-            - Provide examples of past rapid skill acquisition
+#             ## Core Writing Instructions
 
-            #### Experience Quality Over Quantity
-
-            - Emphasize depth and relevance of experience
-            - Highlight specific achievements and impact
-            - Show progression and growth in current role
-
-            ## Output Rules
+#             ### Content Strategy (German Market Focus)
+
+#             1. **Opening:** State the position you're applying for and briefly explain your interest in the company mentioned in the job details
+#             2. **Body:** Focus on what value you bring to the company (not what you can gain from them)
+#             3. **Skills Gap Handling:** If you lack certain required skills, show eagerness to learn and provide examples of quick skill acquisition
+#             4. **Experience Mismatch:** If years of experience don't perfectly match (e.g., job asks 5+, you have ~4.5), emphasize the quality and relevance of your experience
+#             5. **Closing:** Professional closing with availability and next steps
+
+#             ### Language Requirements
+
+#             - **Tone:** Professional and formal (German market prefers formal tone over conversational)
+#             - **Length:** Concise and focused (3 paragraphs preferred for German market, maximum 4 paragraphs)
+#             - **Total word count:** 250-400 words maximum
+#             - **Per paragraph:** 70-100 words (3-4 sentences each)
+#             - **Opening:** 50-70 words
+#             - **Body:** 80-100 words  
+#             - **Closing:** 50-70 words
+#             - **Format:** Use continuous text only - no bullet points or lists within the paragraph body
+#             - **Avoid These Overused Words:**
+#             - Robust → use "strong," "reliable," or "sturdy"
+#             - Keen → use "eager," "interested," or "enthusiastic"  
+#             - Scalable → use "that can handle growth"
+#             - Leverage → use "use"
+#             - Utilize → use "use"
+#             - Innovative → describe specific innovation
+#             - Optimal → use "best," "ideal," or "most effective"
+#             - Capability → use "ability," "feature," or "function"
+#             - Interface → use "connect," "work with," or describe interaction
+#             - Efficient → use "quick," "streamlined," or describe time savings
+#             - Paradigm → use "model," "approach," or "way of thinking"
+#             - Facilitate → use "help," "make easier," or "support"
+#             - Optimize → use "improve," "refine," or describe enhancement
+
+#             ### Specific Content Elements to Include When Relevant
+
+#             #### German Language Skills
+
+#             - Mention motivation for learning German and timeline goals
+#             - Address language barrier concerns proactively
+
+#             #### Technology Learning Appetite
+
+#             - Show willingness to learn new technologies (e.g., SAP, Vue.js, Ai, LLMs)
+#             - Highlight strong fundamentals that enable quick learning
+#             - Provide examples of past rapid skill acquisition
+
+#             #### Experience Quality Over Quantity
+
+#             - Emphasize depth and relevance of experience
+#             - Highlight specific achievements and impact
+#             - Show progression and growth in current role
+
+#             ## Output Rules
+
+#             1. **Content Only:** Provide only the greeting and 3-paragraph body content - no headers, contact information, date, subject line, or closing signature
+#             2. **Customization:** Every paragraph must be specifically tailored to the job description
+#             3. **Authenticity:** Write like a real human - natural flow, simple language, avoid overly polished corporate speak
+#             4. **Specificity:** Reference specific company details, role requirements, and technologies mentioned in the JD
+
+#             ## Quality Checklist
+
+#             Before finalizing, ensure the paragraphs meet these criteria:
+
+#             - [ ] Addresses the specific role and company mentioned in the job details by name
+#             - [ ] Highlights 2-3 most relevant experiences from the CV
+#             - [ ] Addresses any obvious skill gaps with learning commitment
+#             - [ ] Uses natural, varied language (avoiding the banned word list)
+#             - [ ] Uses simple, clear language that sounds human-written (not AI-generated)
+#             - [ ] Avoids overly sophisticated vocabulary or complex sentence structures
+#             - [ ] Includes a clear call to action
+#             - [ ] Stays within 3-4 paragraph limit
+
+#             Remember: Your goal is to write paragraphs that sounds like it was written by a real person with good English skills, not by AI. Keep it natural, straightforward, and genuine.
+
+#             """
+
+
+#         print(len(prompt),'length')
+
+#         messages = [
+#         {"role": "system", "content": prompt}
+#         ]
+#         # Apply chat template
+#         text = tokenizer.apply_chat_template(
+#             messages,
+#             tokenize=False,
+#             add_generation_prompt=True
+#         )
+
+#         # Tokenize input
+#         model_inputs = tokenizer(
+#             [text],
+#             return_tensors="pt",
+#             padding=True,
+#             truncation=True,
+#             max_length=16000
+#         ).to(model.device)
+
+#             # Generate with parameters optimized for markdown
+#         generated_ids = model.generate(
+#             **model_inputs,
+#             max_new_tokens=7192,           # Increased for longer markdown content
+#             do_sample=True,
+#             temperature=0.8,              # Slightly lower for more consistent formatting
+#             top_p=0.9,
+#             top_k=50,
+#             repetition_penalty=1.15,      # Higher to avoid repetitive formatting
+#             eos_token_id=tokenizer.eos_token_id,
+#             pad_token_id=tokenizer.pad_token_id
+#         )
+        
+#         generated_only_ids = generated_ids[:, model_inputs.input_ids.shape[1]:]
+
+
+#         # Decode output
+#         response = tokenizer.decode(
+#             generated_only_ids[0], 
+#             skip_special_tokens=True
+#         )
+
+#         return response.strip()
+
+
+
+#     except Exception as e:
+#         return {"error": str(e)}
+
+
+# async def job_details_alteration(job_details:str) -> str:
+#     try:
+#         prompt = f"""
+#     job_description: {job_details}
+
+#     Your task: Extract only the important parts and rewrite them clearly into 4 sections:
+#     - **Responsibilities** (what the candidate will do)  
+#     - **Requirements / Qualifications** (skills, education, experience needed)  
+#     - **Who You Are** (traits, mindset, culture fit)  
+#     - **Preferred Candidate** (optional nice-to-have skills or experience)
+
+#     Rules:
+#     - Remove irrelevant parts such as "About Us", "Why Join Us", "Perks/Benefits", or generic company marketing.
+#     - Keep the output concise and professional.
+#     - If a section is not found, skip it. """
+
+#         messages = [
+#             {"role": "system", "content": prompt}
+#         ]
+#         text = tokenizer.apply_chat_template(
+#             messages,
+#             tokenize=False,
+#             add_generation_prompt=True
+#         )
+#         model_inputs = tokenizer(
+#             [text],
+#             return_tensors="pt",
+#             padding=True,
+#             truncation=True,
+#             max_length=2048
+#         ).to(model.device)
+#         generated_ids = model.generate(
+#             **model_inputs,
+#             max_new_tokens=8192,           
+#             do_sample=True,
+#             temperature=0.5,              
+#             top_p=0.9,
+#             top_k=50,
+#             repetition_penalty=1.15,   
+#             eos_token_id=tokenizer.eos_token_id,
+#             pad_token_id=tokenizer.pad_token_id
+#         )
+#         generated_only_ids = generated_ids[:, model_inputs.input_ids.shape[1]:]
+#         response = tokenizer.decode(
+#             generated_only_ids[0], 
+#             skip_special_tokens=True
+#         )
+#         return response.strip()
+#     except Exception as e:
+#         return {"error": str(e)}
+    
+    
+async def coverLetterGenerativeAIBot(data: GenerateRequest, resume_text: str) -> str:
+    try:
 
-            1. **Content Only:** Provide only the greeting and 3-paragraph body content - no headers, contact information, date, subject line, or closing signature
-            2. **Customization:** Every paragraph must be specifically tailored to the job description
-            3. **Authenticity:** Write like a real human - natural flow, simple language, avoid overly polished corporate speak
-            4. **Specificity:** Reference specific company details, role requirements, and technologies mentioned in the JD
+        user_content = f"""
+        You are a professional cover letter writer.
 
-            ## Quality Checklist
+        ## Input
+        - **Job Title:** {data.job_title}
+        - **Job Description:** {data.job_details}
+        - **Resume:** {resume_text}
 
-            Before finalizing, ensure the paragraphs meet these criteria:
+        ## Task
+        Write ONLY the four body paragraphs of a professional cover letter, following this structure:
 
-            - [ ] Addresses the specific role and company mentioned in the job details by name
-            - [ ] Highlights 2-3 most relevant experiences from the CV
-            - [ ] Addresses any obvious skill gaps with learning commitment
-            - [ ] Uses natural, varied language (avoiding the banned word list)
-            - [ ] Uses simple, clear language that sounds human-written (not AI-generated)
-            - [ ] Avoids overly sophisticated vocabulary or complex sentence structures
-            - [ ] Includes a clear call to action
-            - [ ] Stays within 3-4 paragraph limit
+        1. **Paragraph 1**: Introduction where you're studying, and that you're applying for a student/intern position while studying.
+        2. **Paragraph 2**: Previous experience or academic projects, include 2 to 4 bullet points highlighting key projects or achievements.
+        3. **Paragraph 3**: Why you want to join this company what attracts you to them and what you can contribute.
+        4. **Paragraph 4**: Closing short and to the point, showing enthusiasm and availability.
+        
+        ### Language Requirements
+        
+        - **Tone:** Professional and formal (German market prefers formal tone over conversational)
+        - **Length:** Concise and focused (3 paragraphs preferred for German market, maximum 4 paragraphs)
+        - **Total word count:** 250-400 words maximum
+        - **Per paragraph:** 70-100 words (3-4 sentences each)
+        - **Opening:** 50-70 words
+        - **Body:** 80-100 words  
+        - **Closing:** 50-70 words
+        
+        - **Avoid These Overused Words:**
+        - Robust → use "strong," "reliable," or "sturdy"
+        - Keen → use "eager," "interested," or "enthusiastic"  
+        - Scalable → use "that can handle growth"
+        - Leverage → use "use"
+        - Utilize → use "use"
+        - Innovative → describe specific innovation
+        - Optimal → use "best," "ideal," or "most effective"
+        - Capability → use "ability," "feature," or "function"
+        - Interface → use "connect," "work with," or describe interaction
+        - Efficient → use "quick," "streamlined," or describe time savings
+        - Paradigm → use "model," "approach," or "way of thinking"
+        - Facilitate → use "help," "make easier," or "support"
+        - Optimize → use "improve," "refine," or describe enhancement
+        
+        #### German Language Skills
 
-            Remember: Your goal is to write paragraphs that sounds like it was written by a real person with good English skills, not by AI. Keep it natural, straightforward, and genuine.
+        - Mention motivation for learning German and timeline goals
+        - Address language barrier concerns proactively
 
-            """
+        #### Technology Learning Appetite
 
+        - Show willingness to learn new technologies (e.g., SAP, Vue.js, Ai, LLMs)
+        - Highlight strong fundamentals that enable quick learning
+        - Provide examples of past rapid skill acquisition
 
-        print(len(prompt),'length')
+        ## Important:
+        - Do **not** include greetings (e.g., "Dear Hiring Manager")
+        - Do **not** include sign-offs (e.g., "Sincerely")
+        - Do **not** include headers or formatting
+        - Do **not** add number on paragraphs, also after each paragraph give a one line space gap then starts next paragraph
+        - Return **only** the four paragraphs
+        """
 
+        openAiApiClient = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+        
         messages = [
-        {"role": "system", "content": prompt}
+            {"role": "system", "content": "You are a professional cover letter body paragraphs generator."},
+            {"role": "user", "content": user_content}
         ]
-        # Apply chat template
-        text = tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
-        )
-
-        # Tokenize input
-        model_inputs = tokenizer(
-            [text],
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=16000
-        ).to(model.device)
-
-            # Generate with parameters optimized for markdown
-        generated_ids = model.generate(
-            **model_inputs,
-            max_new_tokens=7192,           # Increased for longer markdown content
-            do_sample=True,
-            temperature=0.8,              # Slightly lower for more consistent formatting
-            top_p=0.9,
-            top_k=50,
-            repetition_penalty=1.15,      # Higher to avoid repetitive formatting
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.pad_token_id
-        )
-        
-        generated_only_ids = generated_ids[:, model_inputs.input_ids.shape[1]:]
-
 
-        # Decode output
-        response = tokenizer.decode(
-            generated_only_ids[0], 
-            skip_special_tokens=True
+        # response = openAiApiClient.chat.completions.create(
+        #     model="gpt-4-0613",
+        #     messages=messages,
+        #     max_tokens=6092
+        # )
+        # Choose model and max token limit
+        model = "gpt-4-0613"
+        max_total_tokens = 8192  # hard limit for gpt-4-0613
+
+        # Load tokenizer
+        encoding = tiktoken.encoding_for_model(model)
+
+        # Count tokens in the messages
+        def count_message_tokens(messages, encoding):
+            tokens_per_message = 3  # Every message object has a cost (role + content + metadata)
+            tokens_per_name = 1     # If 'name' is present
+            total_tokens = 0
+
+            for message in messages:
+                total_tokens += tokens_per_message
+                for key, value in message.items():
+                    total_tokens += len(encoding.encode(value))
+                    if key == "name":
+                        total_tokens += tokens_per_name
+            total_tokens += 3  # Every reply is primed with <|start|>assistant<|message|>
+            return total_tokens
+
+        input_tokens = count_message_tokens(messages, encoding)
+        available_output_tokens = max_total_tokens - input_tokens
+
+        # Ensure we don't go negative
+        if available_output_tokens < 1:
+            raise ValueError("Input messages are too long. Reduce their size.")
+
+        # Call API
+        response = openAiApiClient.chat.completions.create(
+            model=model,
+            messages=messages,
+            max_tokens=available_output_tokens
         )
 
-        return response.strip()
-
-
-
-    except Exception as e:
-        return {"error": str(e)}
-
-
-async def job_details_alteration(job_details:str) -> str:
-    try:
-        prompt = f"""
-    job_description: {job_details}
-
-    Your task: Extract only the important parts and rewrite them clearly into 4 sections:
-    - **Responsibilities** (what the candidate will do)  
-    - **Requirements / Qualifications** (skills, education, experience needed)  
-    - **Who You Are** (traits, mindset, culture fit)  
-    - **Preferred Candidate** (optional nice-to-have skills or experience)
+        ai_text = response.choices[0].message.content
+        
+        return ai_text
 
-    Rules:
-    - Remove irrelevant parts such as "About Us", "Why Join Us", "Perks/Benefits", or generic company marketing.
-    - Keep the output concise and professional.
-    - If a section is not found, skip it. """
 
-        messages = [
-            {"role": "system", "content": prompt}
-        ]
-        text = tokenizer.apply_chat_template(
-            messages,
-            tokenize=False,
-            add_generation_prompt=True
-        )
-        model_inputs = tokenizer(
-            [text],
-            return_tensors="pt",
-            padding=True,
-            truncation=True,
-            max_length=2048
-        ).to(model.device)
-        generated_ids = model.generate(
-            **model_inputs,
-            max_new_tokens=8192,           
-            do_sample=True,
-            temperature=0.5,              
-            top_p=0.9,
-            top_k=50,
-            repetition_penalty=1.15,   
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.pad_token_id
-        )
-        generated_only_ids = generated_ids[:, model_inputs.input_ids.shape[1]:]
-        response = tokenizer.decode(
-            generated_only_ids[0], 
-            skip_special_tokens=True
-        )
-        return response.strip()
     except Exception as e:
-        return {"error": str(e)}
+        print(f"Error: {e}")
+        return ""

app/services/hf_storage_service.py

@@ -18,11 +18,11 @@ class HuggingFaceStorageService:
             raise ValueError("Folder must be 'resumes' or 'cover-letters'")
 
         if filename is None:
-            filename = f"{uuid.uuid4().hex}.pdf"
+            filename = f"{uuid.uuid4().hex}"
 
         # Create a unique path with date
         timestamp = datetime.now().strftime("%Y/%m/%d")
-        file_path = f"{folder}/{timestamp}/{filename}"
+        file_path = f"{folder}/{timestamp}/{filename}.pdf"
 
         # Save bytes to temp file
         with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(filename)[1]) as temp_file:

app/services/pdf_creator.py

@@ -1,7 +1,5 @@
 from fpdf import FPDF
 import os
-from datetime import datetime
-import io
 from app.services.hf_storage_service import HuggingFaceStorageService
 import unicodedata
 import markdown2
@@ -61,9 +59,16 @@ def build_cover_letter_md(
     job_title, company_name, generated_paragraphs
 ):
     template = f"""
+{your_name}
+{postal_code}, {city}
+{email} | {phone}
 
-{generated_paragraphs}  
+Dear {company_name},
 
+{generated_paragraphs}
+
+Warm Regards,
+{your_name}  
 """
     return template
 
@@ -74,3 +79,5 @@ def convert_md_to_text(md_text: str) -> str:
     html = markdown2.markdown(md_text)
     plain_text = "".join(html.split("<")[0] if "<" in html else html for html in html.split(">"))
     return plain_text
+
+

requirements.txt

@@ -9,4 +9,7 @@ safetensors
 python-multipart
 huggingface_hub
 python-dotenv
-markdown2
+markdown2
+langchain
+openai
+tiktoken