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import os
import argparse
from pathlib import Path
import pandas as pd
parser = argparse.ArgumentParser()
parser.add_argument("--tmpdir", type=str, default=None, help="Temporary directory for Gradio uploads")
args, _ = parser.parse_known_args()
tmpdir = args.tmpdir or os.environ.get("GRADIO_TMPDIR") or "./gradio_tmp"
Path(tmpdir).mkdir(parents=True, exist_ok=True)
os.environ["TMPDIR"] = tmpdir
print(f"Using TMPDIR: {tmpdir}")
import gradio as gr
from inference import run_single_similarity, run_batch_similarity
from data import load_text
from model import load_model
from explanation import result_explanation
pipe = load_model(model_path='vilm/vietcuna-3b-v2')
# pipe = load_model(model_path='kaitchup/Llama-3-8b-awq-4bit')
def gradio_inference(jd_file, cv_files, top_k,
w_exp, w_skills, w_culture, w_traits, w_edu):
jd_path = jd_file.name
jd_text = load_text(jd_path)
weights = {
"Experience & Achievements": w_exp,
"Skills & Competencies": w_skills,
"Culture Fit": w_culture,
"Personal Traits": w_traits,
"Education": w_edu
}
total_weight = sum(weights.values())
if abs(total_weight - 1.0) > 0.1:
warning = f"⚠️ Warning: Total weight is {total_weight:.2f}, please adjust to sum ~1.0"
else:
warning = ""
if len(cv_files) == 1:
cv_path = cv_files[0].name
cv_text = load_text(cv_path)
final_score, section_scores = run_single_similarity(
jd_path=jd_path,
cv_path=cv_path,
SECTION_WEIGHTS=weights,
model_id="sentence-transformers/all-MiniLM-L6-v2",
pipe=pipe
)
prompt = result_explanation(cv_text, jd_text)
explanation = pipe(prompt, max_new_tokens=1024)[0]["generated_text"]
explanation_text = explanation[2]['content'].strip() if isinstance(explanation, list) else explanation
section_text = "\n".join([f"- {k}: {round(v, 4)}" for k, v in section_scores.items()])
return round(final_score, 4), f"{warning}\n\nSection Scores:\n{section_text}\n\nExplanation:\n{explanation_text}", None
else:
cv_paths = [f.name for f in cv_files]
results = run_batch_similarity(
jd_path=jd_path,
cv_input=cv_paths,
SECTION_WEIGHTS=weights,
model_id="sentence-transformers/all-MiniLM-L6-v2",
pipe=pipe
)
results_sorted = sorted(results, key=lambda x: x["Similarity Score"], reverse=True)
for idx, row in enumerate(results_sorted):
row["Rank"] = idx + 1
top_results = results_sorted[:top_k]
df = pd.DataFrame(top_results)
return None, None, df
with gr.Blocks() as demo:
gr.Markdown("## 📄 Resume ⇄ Job Description Matching Tool (LLM Summary + Adjustable Weights)")
with gr.Row():
jd_input = gr.File(label="Upload Job Description (PDF or TXT)", file_types=[".pdf", ".txt"])
cv_input = gr.File(label="Upload Resumes (PDF or TXT)", file_types=[".pdf", ".txt"], file_count="multiple")
gr.Markdown("### 🎯 Section Weights (Total should roughly sum to 1.0)")
with gr.Row():
w_exp = gr.Number(label="Experience & Achievements", value=0.2, step=0.01)
w_skills = gr.Number(label="Skills & Competencies", value=0.2, step=0.01)
w_culture = gr.Number(label="Culture Fit", value=0.2, step=0.01)
w_traits = gr.Number(label="Personal Traits", value=0.2, step=0.01)
w_edu = gr.Number(label="Education", value=0.2, step=0.01)
top_n_slider = gr.Slider(minimum=1, maximum=20, step=1, value=5, label="Top N Results (Batch Mode Only)")
score_output = gr.Number(label="Similarity Score (Single Mode Only)")
explanation_output = gr.Textbox(label="Section Scores + Explanation", lines=15)
dataframe_output = gr.Dataframe(label="Batch Similarity Results", interactive=False)
run_button = gr.Button("Run Matching")
run_button.click(
fn=gradio_inference,
inputs=[
jd_input, cv_input, top_n_slider,
w_exp, w_skills, w_culture, w_traits, w_edu
],
outputs=[score_output, explanation_output, dataframe_output]
)
if __name__ == "__main__":
#demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
demo.launch(server_name="0.0.0.0", share=False)
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