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# app.py
import gradio as gr
import requests
# Replace 'YOUR_GEMINI_API_KEY' with your actual Gemini API key.
GEMINI_API_KEY = "AIzaSyB05WLxtH1x4fMvB87M-GggjQlBnm3YWeE"
# This function sends the resume and job description to the Gemini API
# and returns an optimized resume tailored to the job description.
def optimize_resume(resume, job_description):
# Gemini API endpoint for text generation (update if needed)
url = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"
# Prepare the prompt for the AI model
prompt = (
"You are a professional resume writer. "
"Given the following resume and job description, rewrite the resume to be optimized for the job. "
"Keep the formatting clear and professional. "
"Only output the improved resume.\n\n"
f"Resume:\n{resume}\n\n"
f"Job Description:\n{job_description}\n"
)
# Prepare the request payload for Gemini API
data = {
"contents": [
{
"parts": [
{"text": prompt}
]
}
]
}
# Set the headers, including the API key for authentication
headers = {
"Content-Type": "application/json",
"x-goog-api-key": GEMINI_API_KEY
}
# Send the POST request to Gemini API
response = requests.post(url, headers=headers, json=data)
# If the request is successful, extract and return the optimized resume
if response.status_code == 200:
result = response.json()
# Extract the generated text from the response
try:
optimized_resume = result["candidates"][0]["content"]["parts"][0]["text"]
return optimized_resume
except (KeyError, IndexError):
return "Error: Unexpected response format from Gemini API."
else:
# If there's an error, return the error message
return f"Error: {response.status_code} - {response.text}"
# Create the Gradio interface
with gr.Blocks() as demo:
gr.Markdown(
"""
# AI Resume Optimizer
Paste your resume and the job description below.
The AI will rewrite your resume to better match the job!
"""
)
# Input for the user's resume
resume_input = gr.Textbox(
label="Paste your Resume",
lines=15,
placeholder="Paste your resume here..."
)
# Input for the job description
job_desc_input = gr.Textbox(
label="Paste the Job Description",
lines=10,
placeholder="Paste the job description here..."
)
# Output box for the optimized resume
optimized_resume_output = gr.Textbox(
label="Optimized Resume",
lines=15
)
# Button to submit the inputs and get the optimized resume
submit_btn = gr.Button("Optimize Resume")
# When the button is clicked, call the optimize_resume function
submit_btn.click(
fn=optimize_resume,
inputs=[resume_input, job_desc_input],
outputs=optimized_resume_output
)
# Run the Gradio app
if __name__ == "__main__":
demo.launch()
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