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import gradio as gr
import json
from datetime import datetime
import os
import tempfile
# Import the resume agent (assuming the previous code is saved as resume_agent.py)
from resume_agent import ResumeAgent, get_sample_resume, get_sample_job_description
class GradioResumeApp:
"""Gradio interface for the Resume Optimization Agent"""
def __init__(self):
self.agent = ResumeAgent()
self.sample_resume = get_sample_resume()
self.sample_job_desc = get_sample_job_description()
def process_resume(self, resume_file, resume_text, job_file, job_text, api_key):
"""Process resume optimization request"""
# Validate API key
if not api_key or api_key.strip() == "":
return self._create_error_output("β Please provide a valid Gemini API key")
# Set API key
import google.generativeai as genai
try:
genai.configure(api_key=api_key.strip())
except Exception as e:
return self._create_error_output(f"β Invalid API key: {str(e)}")
# Get resume content
resume_content = self._get_content(resume_file, resume_text, self.sample_resume, "resume")
if not resume_content:
return self._create_error_output("β No resume content provided")
# Get job description content
job_content = self._get_content(job_file, job_text, self.sample_job_desc, "job description")
try:
# Process optimization
results = self.agent.optimize_resume(resume_content, job_content)
# Format results for display
return self._format_results(results)
except Exception as e:
return self._create_error_output(f"β Error during optimization: {str(e)}")
def _get_content(self, file, text, sample, content_type):
"""Extract content from file or text input"""
if file is not None:
try:
content = file.decode('utf-8') if isinstance(file, bytes) else file.read()
return content
except Exception as e:
print(f"Error reading {content_type} file: {str(e)}")
if text and text.strip():
return text.strip()
return sample
def _create_error_output(self, error_message):
"""Create error output tuple"""
return (
error_message, # summary
"", # experience_analysis
"", # keyword_analysis
"", # design_suggestions
"", # editing_suggestions
"" # full_results
)
def _format_results(self, results):
"""Format optimization results for Gradio display"""
# New Summary
new_summary = results.get("new_summary", "No summary generated")
# Experience Analysis
experience_analysis = ""
if "experience_matching" in results:
exp_data = results["experience_matching"]
if isinstance(exp_data, dict) and "ranked_experiences" in exp_data:
experience_analysis = "π **Experience Relevance Analysis:**\n\n"
for i, exp in enumerate(exp_data["ranked_experiences"][:3], 1):
if isinstance(exp, dict):
score = exp.get("relevance_score", "N/A")
points = exp.get("matching_points", [])
experience_analysis += f"**Experience {i}:** Score {score}/10\n"
experience_analysis += f"Key matches: {', '.join(points[:3])}\n\n"
else:
experience_analysis = "Experience analysis completed"
# Keyword Analysis
keyword_analysis = ""
if "keyword_optimization" in results:
kw_data = results["keyword_optimization"]
if isinstance(kw_data, dict):
ats_score = kw_data.get("ats_score", "N/A")
missing_kw = kw_data.get("missing_keywords", [])
keyword_analysis = f"π― **ATS Optimization Score:** {ats_score}/100\n\n"
if missing_kw:
keyword_analysis += f"**Missing Keywords:** {', '.join(missing_kw[:10])}\n\n"
recommendations = kw_data.get("recommendations", [])
if recommendations:
keyword_analysis += "**Recommendations:**\n"
for rec in recommendations[:3]:
keyword_analysis += f"β’ {rec}\n"
else:
keyword_analysis = "Keyword optimization completed"
# Design Suggestions
design_suggestions = ""
if "design_suggestions" in results:
design_data = results["design_suggestions"]
if isinstance(design_data, dict):
template = design_data.get("recommended_template", "Standard")
layout_tips = design_data.get("layout_suggestions", [])
design_suggestions = f"π¨ **Recommended Template:** {template}\n\n"
if layout_tips:
design_suggestions += "**Layout Suggestions:**\n"
for tip in layout_tips[:5]:
design_suggestions += f"β’ {tip}\n"
else:
design_suggestions = "Design suggestions generated"
# Editing Suggestions
editing_suggestions = ""
if "editing_suggestions" in results:
edit_data = results["editing_suggestions"]
if isinstance(edit_data, dict):
score = edit_data.get("overall_score", "N/A")
feedback = edit_data.get("summary_feedback", "")
editing_suggestions = f"βοΈ **Overall Quality Score:** {score}/100\n\n"
if feedback:
editing_suggestions += f"**Feedback:** {feedback}\n\n"
grammar_errors = edit_data.get("grammar_errors", [])
if grammar_errors:
editing_suggestions += "**Grammar Improvements:**\n"
for error in grammar_errors[:3]:
if isinstance(error, dict):
original = error.get("original", "")
corrected = error.get("corrected", "")
editing_suggestions += f"β’ '{original}' β '{corrected}'\n"
else:
editing_suggestions = "Editing analysis completed"
# Full Results (JSON)
full_results = json.dumps(results, indent=2, default=str)
return (
new_summary,
experience_analysis,
keyword_analysis,
design_suggestions,
editing_suggestions,
full_results
)
def create_interface(self):
"""Create and return Gradio interface"""
with gr.Blocks(
title="AI Resume Optimizer",
theme=gr.themes.Soft(),
css="""
.gradio-container {
max-width: 1200px !important;
}
.main-header {
text-align: center;
margin-bottom: 30px;
}
"""
) as interface:
gr.HTML("""
<div class="main-header">
<h1>π AI Resume Optimization Agent</h1>
<p>Upload your resume and job description to get AI-powered optimization suggestions</p>
</div>
""")
with gr.Row():
with gr.Column(scale=1):
gr.HTML("<h2>π Input</h2>")
# API Key input
api_key = gr.Textbox(
label="π Gemini API Key",
placeholder="Enter your Gemini API key here...",
type="password",
info="Get your free API key from Google AI Studio"
)
# Resume input
with gr.Tab("Resume Upload"):
resume_file = gr.File(
label="Upload Resume (PDF/TXT/DOCX)",
file_types=[".pdf", ".txt", ".docx"]
)
with gr.Tab("Resume Text"):
resume_text = gr.Textbox(
label="Paste Resume Text",
placeholder="Paste your resume content here...",
lines=8,
value=self.sample_resume
)
# Job description input
with gr.Tab("Job Description Upload"):
job_file = gr.File(
label="Upload Job Description",
file_types=[".pdf", ".txt", ".docx"]
)
with gr.Tab("Job Description Text"):
job_text = gr.Textbox(
label="Paste Job Description",
placeholder="Paste job description here...",
lines=6,
value=self.sample_job_desc
)
# Optimize button
optimize_btn = gr.Button(
"π Optimize Resume",
variant="primary",
size="lg"
)
with gr.Column(scale=1):
gr.HTML("<h2>π Results</h2>")
with gr.Tab("β¨ New Summary"):
summary_output = gr.Textbox(
label="Optimized Professional Summary",
lines=4,
interactive=False
)
with gr.Tab("π Experience Analysis"):
experience_output = gr.Markdown(
label="Experience Relevance Analysis"
)
with gr.Tab("π― Keywords & ATS"):
keyword_output = gr.Markdown(
label="Keyword Optimization & ATS Score"
)
with gr.Tab("π¨ Design Tips"):
design_output = gr.Markdown(
label="Design & Formatting Suggestions"
)
with gr.Tab("βοΈ Editing Tips"):
editing_output = gr.Markdown(
label="Grammar & Content Improvements"
)
with gr.Tab("π Full Report"):
full_output = gr.Code(
label="Complete Analysis (JSON)",
language="json"
)
# Event handlers
optimize_btn.click(
fn=self.process_resume,
inputs=[resume_file, resume_text, job_file, job_text, api_key],
outputs=[
summary_output,
experience_output,
keyword_output,
design_output,
editing_output,
full_output
]
)
# Example section
with gr.Row():
gr.HTML("""
<div style="margin-top: 30px; padding: 20px; background-color: #f8f9fa; border-radius: 10px;">
<h3>π‘ Quick Start Guide:</h3>
<ol>
<li>Get your free Gemini API key from <a href="https://makersuite.google.com/app/apikey" target="_blank">Google AI Studio</a></li>
<li>Upload your resume or use the sample provided</li>
<li>Add a job description you're targeting (optional)</li>
<li>Click "Optimize Resume" to get AI-powered suggestions</li>
</ol>
<p><strong>Features:</strong> Professional Summary Generation β’ Experience Matching β’ ATS Optimization β’ Design Suggestions β’ Grammar & Style Improvements</p>
</div>
""")
return interface
def main():
"""Launch the Gradio app"""
app = GradioResumeApp()
interface = app.create_interface()
# Launch the app
interface.launch(
server_name="0.0.0.0", # Allow external access
server_port=7860,
share=True, # Create public link
debug=True
)
if __name__ == "__main__":
main()
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