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app.py

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+import os
+import pdfplumber
+import google.generativeai as genai
+from dotenv import load_dotenv
+import json 
+import gradio as gr
+
+# --- NEW: "Dark Mode" Custom CSS ---
+custom_css = """
+/* A modern, clean "Dark Mode" theme */
+body {
+    /* A dark gradient background */
+    background: linear-gradient(135deg, #1e1e1e 0%, #121212 100%);
+    font-family: 'Inter', 'Segoe UI', 'Roboto', sans-serif;
+}
+
+/* Style the main app container with a "dark glass" effect */
+.gradio-container {
+    border: none !important;
+    border-radius: 12px !important;
+    box-shadow: 0 8px 32px 0 rgba(0, 0, 0, 0.3) !important;
+    background-color: rgba(30, 30, 30, 0.85) !important;
+    backdrop-filter: blur(10px) !important;
+    border: 1px solid rgba(255, 255, 255, 0.1) !important;
+}
+
+/* Style the primary 'generate' buttons (Blue stands out well on dark) */
+button[data-testid="button-primary"] {
+    background: linear-gradient(90deg, #3A7BD5 0%, #00D2FF 100%);
+    color: white;
+    border-radius: 8px;
+    font-weight: bold;
+    box-shadow: 0 4px 14px 0 rgba(0, 118, 255, 0.39);
+    border: none !important;
+    transition: all 0.3s ease;
+}
+button[data-testid="button-primary"]:hover {
+    box-shadow: 0 6px 20px 0 rgba(0, 118, 255, 0.5);
+    transform: translateY(-2px);
+}
+
+/* --- FIX FOR TAB TITLES --- */
+[data-testid="tab-button"] {
+    color: #a0a0a0 !important; /* Light grey for unselected tabs */
+    font-weight: 600 !important;
+}
+[data-testid="tab-button"].selected {
+    color: #ffffff !important; /* White for selected tab */
+    border-bottom: 2px solid #00D2FF !important; /* Accent color for the underline */
+}
+
+/* Style ALL markdown boxes (inputs and outputs) */
+[data-testid="markdown"] {
+    background-color: #2a2a2a !important; /* Dark grey for boxes */
+    border-radius: 8px !important;
+    border: 1px solid #444 !important;
+    padding: 20px !important;
+    box-shadow: 0 2px 4px 0 rgba(0,0,0,0.1);
+}
+
+/* Force all text within markdown components to be light */
+[data-testid="markdown"] p, 
+[data-testid="markdown"] h1, 
+[data-testid="markdown"] h2, 
+[data-testid="markdown"] h3, 
+[data-testid="markdown"] li, 
+[data-testid="markdown"] ol, 
+[data-testid="markdown"] ul {
+    color: #e0e0e0 !important; /* Light grey text */
+}
+
+/* Style the input textboxes and file upload */
+[data-testid="textbox"] textarea, .gradio-file {
+    background-color: #2a2a2a !important;
+    border-radius: 8px !important;
+    border: 1px solid #444 !important;
+    color: #e0e0e0 !important; /* Make typed text light */
+}
+/* Style the labels for inputs (e.g., "Paste the Job Description Here") */
+.gradio-form > * > label {
+    color: #a0a0a0 !important;
+    font-weight: 500 !important;
+}
+"""
+
+# --- (All 6 Agents and Helper Functions) ---
+# (No changes to the agent functions themselves)
+
+def setup_api_key():
+    """
+    Loads the Google API key from the .env file and configures the SDK.
+    """
+    try:
+        load_dotenv() # Loads environment variables from .env
+        api_key = os.getenv("GOOGLE_API_KEY")
+        
+        if not api_key:
+            print("Error: GOOGLE_API_KEY not found.")
+            print("Please create a .env file in the project root and add:")
+            print("GOOGLE_API_KEY=YOUR_API_KEY_HERE")
+            return False
+            
+        genai.configure(api_key=api_key)
+        print("API Key configured successfully.")
+        return True
+        
+    except Exception as e:
+        print(f"Error during API configuration: {e}")
+        return False
+
+def extract_text_from_file(file_path):
+    """
+    Extracts text from an uploaded file (.pdf or .txt).
+    """
+    text = ""
+    try:
+        file_extension = os.path.splitext(file_path)[1].lower()
+
+        if file_extension == '.pdf':
+            print(f"Extracting text from PDF: {file_path}")
+            with pdfplumber.open(file_path) as pdf:
+                for i, page in enumerate(pdf.pages):
+                    page_text = page.extract_text()
+                    if page_text:
+                        text += page_text + "\n"
+            print("PDF extraction complete.")
+            
+        elif file_extension == '.txt':
+            print(f"Extracting text from TXT: {file_path}")
+            with open(file_path, 'r', encoding='utf-8') as f:
+                text = f.read()
+            print("TXT extraction complete.")
+            
+        else:
+            return "Unsupported file format. Please upload a .txt or .pdf file."
+        
+        if not text.strip():
+            return "Error: File is empty or text could not be extracted."
+            
+        return text
+    
+    except Exception as e:
+        print(f"Error reading file {file_path}: {e}")
+        return f"Error reading file. It may be corrupted or in an unsupported format."
+
+def analyze_job_description(jd_text):
+    """
+    Agent 1: Analyzes the job description (JD).
+    """
+    model = genai.GenerativeModel('models/gemini-flash-latest')
+    prompt = f"""
+    You are a Senior Technical Recruiter. Analyze the following job description (JD)
+    and extract the most critical information.
+    
+    Return your analysis as a JSON object, and NOTHING ELSE. Do not add "```json".
+    
+    The JSON structure must be:
+    {{
+      "job_title": "string",
+      "company": "string",
+      "key_responsibilities": ["list", "of", "strings"],
+      "hard_skills_keywords": ["list", "of", "tech", "skills"],
+      "soft_skills_keywords": ["list", "of", "interpersonal", "skills"],
+      "company_tone": "e.g., 'Formal & Corporate', 'Fast-paced & Startup'"
+    }}
+    
+    Job Description Text:
+    ---
+    {jd_text}
+    ---
+    """
+    try:
+        print("\nSending JD to AI Recruiter Agent...")
+        response = model.generate_content(prompt)
+        cleaned_response_text = response.text.strip().replace("```json\n", "").replace("\n```", "").strip()
+        print("AI analysis complete.")
+        jd_analysis = json.loads(cleaned_response_text)
+        return jd_analysis
+    except Exception as e:
+        print(f"Error during AI analysis (Recruiter Agent): {e}")
+        return None
+
+def analyze_resume(resume_text, jd_analysis_json):
+    """
+    Agent 2: Analyzes the resume against the JD's analysis.
+    """
+    model = genai.GenerativeModel('models/gemini-flash-latest')
+    jd_analysis_string = json.dumps(jd_analysis_json, indent=2)
+    prompt = f"""
+    You are an expert Career Coach. You are given a user's resume and a JSON analysis
+    of their target job. Your task is to find all relevant experiences and identify gaps.
+    
+    Return your analysis as a JSON object, and NOTHING ELSE. Do not add "```json".
+    
+    The JSON structure must be:
+    {{
+      "matching_experiences": ["list of text snippets from the resume that are highly relevant"],
+      "quantifiable_achievements_found": ["list of bullet points from the resume that already have numbers"],
+      "critical_gaps": ["list of key hard skills or responsibilities from the JD that are NOT mentioned in the resume"]
+    }}
+    
+    ---
+    Target Job Analysis (JSON):
+    {jd_analysis_string}
+    ---
+    User's Resume Text:
+    {resume_text}
+    ---
+    """
+    try:
+        print("\nSending Resume and JD to AI Career Coach Agent...")
+        response = model.generate_content(prompt)
+        cleaned_response_text = response.text.strip().replace("```json\n", "").replace("\n```", "").strip()
+        print("AI career coach analysis complete.")
+        resume_analysis = json.loads(cleaned_response_text)
+        return resume_analysis
+    except Exception as e:
+        print(f"Error during AI analysis (Career Coach Agent): {e}")
+        return None
+
+def generate_tailored_resume(resume_text, jd_analysis_json, resume_analysis_json):
+    """
+    Agent 3: The "Master Rewrite Agent."
+    """
+    model = genai.GenerativeModel('models/gemini-flash-latest')
+    jd_analysis_string = json.dumps(jd_analysis_json, indent=2)
+    resume_analysis_string = json.dumps(resume_analysis_json, indent=2)
+    prompt = f"""
+    You are a world-class Professional Resume Writer. Your task is to rewrite the "Experience"
+    section of the resume to align *perfectly* with a target job.
+    
+    **CRITICAL RULES:**
+    1.  **Do NOT invent new experiences.** You must only rewrite and re-phrase the
+        *existing* experience from the original resume.
+    2.  Integrate the `hard_skills_keywords` from the Job Analysis naturally into the bullet points.
+    3.  Rewrite weak bullet points to use the **STAR method** (Situation, Task, Action, Result)
+        and quantify achievements where possible.
+    4.  Reflect the `company_tone` from the Job Analysis in your writing style.
+    5.  **Output *only* the new, fully rewritten 'Experience' section** in clean
+        Markdown format. Do not add *any* other text, headings, or explanations.
+    
+    ---
+    [Target Job Analysis (JSON)]
+    {jd_analysis_string}
+    ---
+    [Career Coach's Gap Analysis (JSON)]
+    {resume_analysis_string}
+    ---
+    [Original Resume Text]
+    {resume_text}
+    ---
+    
+    Provide the rewritten "Experience" section in Markdown:
+    """
+    try:
+        print("\nSending all data to Master Rewrite Agent...")
+        response = model.generate_content(prompt)
+        print("Master rewrite complete.")
+        return response.text.strip()
+    except Exception as e:
+        print(f"Error during AI analysis (Rewrite Agent): {e}")
+        return None
+
+def generate_ats_report(tailored_resume_text, jd_analysis_json):
+    """
+    Agent 4: The "ATS Scorecard Agent."
+    """
+    model = genai.GenerativeModel('models/gemini-flash-latest')
+    jd_analysis_string = json.dumps(jd_analysis_json, indent=2)
+    
+    prompt = f"""
+    You are an ATS (Applicant Tracking System) scanner. Your task is to generate a
+    'Tailor Report' in Markdown format that scores the "New Resume Section" against
+    the "Target Job Analysis."
+    
+    **Output Format (Must be Markdown):**
+    
+    ### Your 'Tailor' Report
+    
+    **ATS Match Score:** 85%
+    
+    **Keywords Hit:**
+    - Python
+    - Django
+    
+    **Keywords Missing:**
+    - Flask
+    - Data Analytics
+    
+    **Suggestion:** Great job! Consider adding a project or skill that mentions 'Flask'.
+    
+    ---
+    [Target Job Analysis (JSON)]
+    {jd_analysis_string}
+    ---
+    [New Resume Section]
+    {tailored_resume_text}
+    ---
+    
+    Now, generate the 'Tailor Report' in Markdown, and NOTHING ELSE.
+    """
+    
+    try:
+        print("\nSending data to ATS Scorecard Agent...")
+        response = model.generate_content(prompt)
+        print("ATS report complete.")
+        return response.text.strip()
+    except Exception as e:
+        print(f"Error during AI analysis (ATS Agent): {e}")
+        return None
+
+def generate_cover_letter(resume_text, jd_analysis_json, resume_analysis_json):
+    """
+    Agent 5: The "Cover Letter Agent."
+    """
+    
+    model = genai.GenerativeModel('models/gemini-flash-latest')
+    
+    jd_analysis_string = json.dumps(jd_analysis_json, indent=2)
+    resume_analysis_string = json.dumps(resume_analysis_json, indent=2)
+
+    prompt = f"""
+    You are a world-class Professional Resume Writer and Career Coach.
+    Your task is to write a compelling, professional cover letter for a job applicant
+    based on their resume and a target job description.
+    
+    **CRITICAL RULES:**
+    1.  **Tone:** The tone must be professional, confident, and aligned with the `company_tone`
+        from the Job Analysis.
+    2.  **Structure:** Follow a standard cover letter format (Salutation, Introduction, Body, Conclusion, Sign-off).
+    3.  **Body Paragraphs:**
+        * In the first body paragraph, highlight the applicant's skills that
+            match the `hard_skills_keywords` and `key_responsibilities`.
+        * In the second body paragraph, use the `matching_experiences` to provide a
+            specific example or story that proves their qualification.
+        * Address any `critical_gaps` by framing them positively, e.g., "While my
+            direct experience with 'Flask' is developing, my proven ability to master
+            'Django' and other Python frameworks demonstrates my capacity to learn quickly..."
+    4.  **Do NOT invent new experiences.** You must only use information from the
+        "Original Resume Text" and the analyses.
+    5.  **Output *only* the cover letter** in clean Markdown format. Do not add
+        any other text, headings, or explanations.
+    
+    ---
+    [Target Job Analysis (JSON)]
+    {jd_analysis_string}
+    ---
+    [Career Coach's Gap Analysis (JSON)]
+    {resume_analysis_string}
+    ---
+    [Original Resume Text]
+    {resume_text}
+    ---
+    
+    Now, provide the complete, professional cover letter in Markdown:
+    """
+
+    try:
+        print("\nSending all data to Cover Letter Agent...")
+        response = model.generate_content(prompt)
+        print("Cover letter generation complete.")
+        return response.text.strip()
+        
+    except Exception as e:
+        print(f"Error during AI analysis (Cover Letter Agent): {e}")
+        return None
+
+def generate_interview_prep(jd_analysis_json, resume_analysis_json):
+    """
+    Agent 6: The "Hiring Manager Agent."
+    Generates custom interview questions based on the job and the candidate's gaps.
+    """
+    
+    model = genai.GenerativeModel('models/gemini-flash-latest')
+    
+    jd_analysis_string = json.dumps(jd_analysis_json, indent=2)
+    resume_analysis_string = json.dumps(resume_analysis_json, indent=2)
+
+    prompt = f"""
+    You are a senior Hiring Manager preparing to interview a candidate. You are given
+    an analysis of the job and an analysis of the candidate's resume.
+    
+    Your task is to generate a custom "Interview Prep Sheet" in Markdown.
+    
+    **Instructions:**
+    1.  Create 2-3 **Behavioral Questions** based on the `soft_skills_keywords`.
+    2.  Create 2-3 **Technical Questions** based on the `hard_skills_keywords`.
+    3.  Create 1-2 **Gap-Based Questions** based on the `critical_gaps`. These are the
+        most important questions to ask. (e.g., "I see you have experience in X, but
+        this role requires Y. Can you tell me how you'd bridge that gap?")
+    4.  For *each* question, provide a brief **"Hint for a strong answer"** that tells
+        the user what the interviewer is *really* looking for.
+    
+    **Output *only* the prep sheet** in clean Markdown.
+    
+    ---
+    [Target Job Analysis (JSON)]
+    {jd_analysis_string}
+    ---
+    [Career Coach's Gap Analysis (JSON)]
+    {resume_analysis_string}
+    ---
+    
+    Now, provide the complete "Interview Prep Sheet" in Markdown:
+    """
+
+    try:
+        print("\nSending all data to Hiring Manager Agent...")
+        response = model.generate_content(prompt)
+        print("Interview prep generation complete.")
+        return response.text.strip()
+        
+    except Exception as e:
+        print(f"Error during AI analysis (Hiring Manager Agent): {e}")
+        return None
+
+
+# --- (Pipeline 1: Resume Tailor) ---
+def tailor_resume_pipeline(resume_file, job_description):
+    """
+    Main controller for the "Resume Tailor" tab.
+    Runs the full 4-agent pipeline (Agents 1, 2, 3, 4).
+    """
+    if not setup_api_key():
+        return "Error: API Key is not configured. Please check your .env file.", ""
+        
+    print("--- RESUME TAILOR PIPELINE INITIATED ---")
+    print("Step 1: Extracting text...")
+    if resume_file is None:
+        return "Error: Please upload a resume file.", ""
+    resume_text = extract_text_from_file(resume_file.name)
+    if "Error" in resume_text or not resume_text:
+        return f"Could not process the resume file. Error: {resume_text}", ""
+    
+    if not job_description:
+        return "Error: Please paste the job description.", ""
+        
+    print("Step 2: Analyzing job description...")
+    jd_data = analyze_job_description(job_description)
+    if not jd_data:
+        return "The AI could not analyze the job description. Please try again.", ""
+        
+    print("Step 3: Analyzing resume...")
+    resume_data = analyze_resume(resume_text, jd_data)
+    if not resume_data:
+        return "The AI could not analyze the resume. Please try again.", ""
+        
+    print("Step 4: Generating tailored resume...")
+    tailored_resume = generate_tailored_resume(resume_text, jd_data, resume_data)
+    if not tailored_resume:
+        return "The AI failed to generate the final resume. Please try again.", ""
+
+    print("Step 5: Generating ATS report...")
+    ats_report = generate_ats_report(tailored_resume, jd_data)
+    if not ats_report:
+        ats_report = "Error: Could not generate ATS report."
+
+    print("Resume pipeline complete! Returning 2 outputs.")
+    return tailored_resume, ats_report
+
+
+# --- (Pipeline 2: Cover Letter) ---
+def generate_cover_letter_pipeline(resume_file, job_description):
+    """
+    Main controller for the "Cover Letter" tab.
+    Runs a 3-agent pipeline (Agents 1, 2, 5).
+    """
+    if not setup_api_key():
+        return "Error: API Key is not configured. Please check your .env file."
+        
+    print("--- COVER LETTER PIPELINE INITIATED ---")
+    print("Step 1: Extracting text...")
+    if resume_file is None:
+        return "Error: Please upload a resume file."
+    resume_text = extract_text_from_file(resume_file.name)
+    if "Error" in resume_text or not resume_text:
+        return f"Could not process the resume file. Error: {resume_text}"
+    
+    if not job_description:
+        return "Error: Please paste the job description."
+        
+    print("Step 2: Analyzing job description...")
+    jd_data = analyze_job_description(job_description)
+    if not jd_data:
+        return "The AI could not analyze the job description. Please try again."
+        
+    print("Step 3: Analyzing resume...")
+    resume_data = analyze_resume(resume_text, jd_data)
+    if not resume_data:
+        return "The AI could not analyze the resume. Please try again."
+        
+    print("Step 4: Generating cover letter...")
+    cover_letter = generate_cover_letter(resume_text, jd_data, resume_data)
+    if not cover_letter:
+        return "The AI failed to generate the cover letter. Please try again."
+
+    print("Cover letter pipeline complete! Returning 1 output.")
+    return cover_letter
+
+
+# --- (Pipeline 3: Interview Prep) ---
+def generate_interview_prep_pipeline(resume_file, job_description):
+    """
+    Main controller for the "Interview Prep" tab.
+    Runs a 3-agent pipeline (Agents 1, 2, 6).
+    """
+    if not setup_api_key():
+        return "Error: API Key is not configured. Please check your .env file."
+        
+    print("--- INTERVIEW PREP PIPELINE INITIATED ---")
+    print("Step 1: Extracting text...")
+    if resume_file is None:
+        return "Error: Please upload a resume file."
+    resume_text = extract_text_from_file(resume_file.name)
+    if "Error" in resume_text or not resume_text:
+        return f"Could not process the resume file. Error: {resume_text}"
+    
+    if not job_description:
+        return "Error: Please paste the job description."
+        
+    print("Step 2: Analyzing job description...")
+    jd_data = analyze_job_description(job_description)
+    if not jd_data:
+        return "The AI could not analyze the job description. Please try again."
+        
+    print("Step 3:Analyzing resume...")
+    resume_data = analyze_resume(resume_text, jd_data)
+    if not resume_data:
+        return "The AI could not analyze the resume. Please. Please try again."
+        
+    print("Step 4: Generating interview prep...")
+    interview_prep = generate_interview_prep(jd_data, resume_data) # Note: Doesn't need resume_text
+    if not interview_prep:
+        return "The AI failed to generate the interview prep. Please try again."
+
+    print("Interview prep pipeline complete! Returning 1 output.")
+    return interview_prep
+
+
+# --- FINAL Gradio Web Interface (with 3 Tabs + Custom UI) ---
+# We use gr.themes.Base() as our starting point and apply our CSS
+with gr.Blocks(theme=gr.themes.Base(), css=custom_css) as demo:
+    gr.Markdown(
+        """
+        <div style="text-align: center; padding: 20px 0;">
+            <h1 style="font-size: 2.5em; font-weight: 700; color: #ffffff;">
+                🤖 AI-Powered Job Application Suite
+            </h1>
+            <p style="font-size: 1.2em; color: #a0a0a0;">
+                From Resume to Interview, get the AI-powered edge you need.
+            </p>
+        </div>
+        """
+    )
+    
+    with gr.Tabs():
+        # --- Tab 1: Resume Tailor ---
+        with gr.TabItem("1. Resume Tailor"):
+            gr.Markdown("Upload your resume and paste a job description to get a tailored 'Experience' section and an ATS report.")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    resume_file_input_1 = gr.File(label="Upload Your Master Resume (.pdf or .txt)")
+                    job_description_input_1 = gr.Textbox(lines=15, label="Paste the Job Description Here")
+                    submit_button_1 = gr.Button("Tailor My Resume!", variant="primary")
+                with gr.Column(scale=2):
+                    tailored_resume_output = gr.Markdown(label="Your New, Tailored Resume Section")
+                    ats_report_output = gr.Markdown(label="Your 'Tailor' Report")
+            
+            submit_button_1.click(
+                fn=tailor_resume_pipeline,
+                inputs=[resume_file_input_1, job_description_input_1],
+                outputs=[tailored_resume_output, ats_report_output]
+            )
+
+        # --- Tab 2: Cover Letter Generator ---
+        with gr.TabItem("2. Cover Letter Generator"):
+            gr.Markdown("Upload your resume and paste a job description to generate a custom cover letter in seconds.")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    resume_file_input_2 = gr.File(label="Upload Your Master Resume (.pdf or .txt)")
+                    job_description_input_2 = gr.Textbox(lines=15, label="Paste the Job Description Here")
+                    submit_button_2 = gr.Button("Generate My Cover Letter!", variant="primary")
+                with gr.Column(scale=2):
+                    cover_letter_output = gr.Markdown(label="Your New, Generated Cover Letter")
+
+            submit_button_2.click(
+                fn=generate_cover_letter_pipeline,
+                inputs=[resume_file_input_2, job_description_input_2],
+                outputs=[cover_letter_output]
+            )
+            
+        # --- Tab 3: Interview Prep ---
+        with gr.TabItem("3. Interview Prep"):
+            gr.Markdown("Upload your resume and paste a job description to get a custom list of interview questions and answer hints.")
+            with gr.Row():
+                with gr.Column(scale=1):
+                    resume_file_input_3 = gr.File(label="Upload Your Master Resume (.pdf or .txt)")
+                    job_description_input_3 = gr.Textbox(lines=15, label="Paste the Job Description Here")
+                    submit_button_3 = gr.Button("Generate My Prep Sheet!", variant="primary")
+                with gr.Column(scale=2):
+                    interview_prep_output = gr.Markdown(label="Your Custom Interview Prep Sheet")
+
+            submit_button_3.click(
+                fn=generate_interview_prep_pipeline,
+                inputs=[resume_file_input_3, job_description_input_3],
+                outputs=[interview_prep_output]
+            )
+
+print("Launching the AI Job Application Suite (v6.0 DARK MODE)...")
+# Added share=True so you can easily open it on your phone or share it
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,4 @@
+google-generativeai
+pdfplumber
+gradio
+python-dotenv