Add application file

app.py

@@ -0,0 +1,188 @@
+# app.py
+
+import os
+import io
+import json
+from datetime import datetime
+
+import streamlit as st
+from transformers import pipeline
+from docx import Document
+from PyPDF2 import PdfReader
+
+# ------------------------------------------------------------------------------
+# Model Pipeline Setup
+# ------------------------------------------------------------------------------
+
+# Use an instruction-tuned model (e.g., Flan-T5-xl)
+model_name = "google/flan-t5-xl"
+instruct_pipeline = pipeline(
+    "text2text-generation", model=model_name, max_length=1024, truncation=True
+)
+
+# ------------------------------------------------------------------------------
+# Helper Functions for File Reading
+# ------------------------------------------------------------------------------
+
+def read_docx(file_bytes: bytes) -> str:
+    """Extract text from a DOCX file."""
+    document = Document(io.BytesIO(file_bytes))
+    return "\n".join([para.text for para in document.paragraphs])
+
+def read_pdf(file_bytes: bytes) -> str:
+    """Extract text from a PDF file."""
+    reader = PdfReader(io.BytesIO(file_bytes))
+    text = []
+    for page in reader.pages:
+        extracted = page.extract_text()
+        if extracted:
+            text.append(extracted)
+    return "\n".join(text)
+
+def read_plain(file_bytes: bytes) -> str:
+    """Extract text from a plain text file."""
+    return file_bytes.decode("utf-8", errors="ignore")
+
+def extract_resume_text(file_obj) -> str:
+    """
+    Determine file type and extract text accordingly.
+    Supports PDF, DOCX, and plain text.
+    """
+    file_bytes = file_obj.read()
+    file_obj.seek(0)  # Reset pointer for potential further use
+    filename = file_obj.name if hasattr(file_obj, "name") else "resume.txt"
+    ext = os.path.splitext(filename)[-1].lower()
+
+    if ext == ".pdf":
+        return read_pdf(file_bytes)
+    elif ext in [".docx", ".doc"]:
+        return read_docx(file_bytes)
+    else:
+        return read_plain(file_bytes)
+
+# ------------------------------------------------------------------------------
+# Core AI Functions
+# ------------------------------------------------------------------------------
+
+def parse_resume(resume_text: str) -> str:
+    """
+    Extract candidate details from the resume text.
+    The model returns a JSON with keys: first_name, last_name, location,
+    work_experience, school_experience, and skills.
+    """
+    prompt = (
+        "You are an expert resume parser. Extract the following keys from the resume text: "
+        "first_name, last_name, location, work_experience, school_experience, and skills. "
+        "Return the answer strictly in JSON format with no extra text.\n\n"
+        f"Resume:\n{resume_text}"
+    )
+    result = instruct_pipeline(prompt, max_length=512)[0]["generated_text"]
+    return result.strip()
+
+def generate_cover_letter(candidate_json: str, job_description: str, date_str: str) -> str:
+    """
+    Generate a cover letter using the candidate's JSON profile, the job description,
+    and the current date.
+    """
+    prompt = (
+        "You are a seasoned career advisor and professional cover letter writer. "
+        "Using the candidate information provided in JSON format, craft a persuasive and personalized cover letter "
+        "tailored to the job description. Structure your answer with an introduction, body, and closing, "
+        "and include the date {date}.\n\n"
+        "Candidate JSON:\n{candidate_json}\n\n"
+        "Job Description:\n{job_description}\n\n"
+        "Cover Letter:".format(
+            candidate_json=candidate_json, job_description=job_description, date=date_str
+        )
+    )
+    result = instruct_pipeline(prompt, max_length=1024)[0]["generated_text"]
+    return result.strip()
+
+def generate_resume(first_name: str, last_name: str, location: str,
+                    work_experience: str, school_experience: str, skills: str) -> str:
+    """
+    Generate a professional resume using candidate information provided in a form.
+    """
+    candidate_info = {
+        "first_name": first_name,
+        "last_name": last_name,
+        "location": location,
+        "work_experience": work_experience,
+        "school_experience": school_experience,
+        "skills": skills,
+    }
+    candidate_str = json.dumps(candidate_info, indent=2)
+    
+    prompt = (
+        "You are a professional resume writer. Using the candidate information provided in JSON, "
+        "create a compelling, well-organized resume in plain text format. Include clear sections for "
+        "personal details, work experience, education, and skills. Ensure the resume is concise and professional.\n\n"
+        "Candidate Information:\n" + candidate_str + "\n\nResume:"
+    )
+    result = instruct_pipeline(prompt, max_length=1024)[0]["generated_text"]
+    return result.strip()
+
+# ------------------------------------------------------------------------------
+# Streamlit UI
+# ------------------------------------------------------------------------------
+
+st.set_page_config(page_title="AI-Powered Job Application Assistant", layout="wide")
+st.title("AI-Powered Job Application Assistant")
+st.markdown("Generate a **Cover Letter** from your resume or **Create a Resume** from scratch using AI.")
+
+# Create tabs for the two functionalities
+tabs = st.tabs(["Cover Letter Generator", "Resume Creator"])
+
+# ----- Tab 1: Cover Letter Generator -----
+with tabs[0]:
+    st.header("Cover Letter Generator")
+    st.markdown("Upload your resume (PDF, DOCX, or TXT) and paste the job description below.")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        resume_file = st.file_uploader("Upload Your Resume", type=["pdf", "docx", "doc", "txt"])
+    
+    with col2:
+        job_description = st.text_area("Job Description", height=200, placeholder="Paste the job description here...")
+    
+    if st.button("Generate Cover Letter"):
+        if resume_file is None or not job_description.strip():
+            st.error("Please upload a resume and enter a job description.")
+        else:
+            with st.spinner("Processing resume..."):
+                resume_text = extract_resume_text(resume_file)
+                candidate_json = parse_resume(resume_text)
+                current_date = datetime.today().strftime("%d - %b - %Y")
+                cover_letter = generate_cover_letter(candidate_json, job_description, current_date)
+            
+            st.subheader("Extracted Candidate Profile (JSON)")
+            st.code(candidate_json, language="json")
+            
+            st.subheader("Generated Cover Letter")
+            st.text_area("", cover_letter, height=300)
+
+# ----- Tab 2: Resume Creator -----
+with tabs[1]:
+    st.header("Resume Creator")
+    st.markdown("Fill in your details below to generate a professional resume from scratch.")
+    
+    with st.form("resume_form"):
+        col1, col2 = st.columns(2)
+        with col1:
+            first_name = st.text_input("First Name", placeholder="John")
+        with col2:
+            last_name = st.text_input("Last Name", placeholder="Doe")
+        
+        location = st.text_input("Location", placeholder="City, State, Country")
+        work_experience = st.text_area("Work Experience", height=150, placeholder="List your past roles and achievements...")
+        school_experience = st.text_area("Education", height=150, placeholder="List your academic qualifications...")
+        skills = st.text_area("Skills", height=100, placeholder="List your key skills...")
+        
+        submit = st.form_submit_button("Generate Resume")
+    
+    if submit:
+        with st.spinner("Generating resume..."):
+            resume_text = generate_resume(first_name, last_name, location, work_experience, school_experience, skills)
+        st.subheader("Generated Resume")
+        st.text_area("", resume_text, height=400)