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import gradio as gr
import pdfplumber
import docx
import os
import pytesseract
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
from huggingface_hub import login
import spaces
# 🔐 Login (Hugging Face token should be set as HF_TOKEN env variable)
login(token=os.environ.get("token"))
# ✅ Check for GPU
if not torch.cuda.is_available():
raise RuntimeError("❌ GPU not detected! Please enable GPU in Space settings.")
print(f"✅ Using GPU: {torch.cuda.get_device_name(0)}")
# Model setup
model_id = "mistralai/Mistral-7B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ.get("token"))
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.float16,
token=os.environ.get("token"),
trust_remote_code=True
)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
# 📄 Extractors
def extract_text_from_pdf(file):
text = ""
with pdfplumber.open(file) as pdf:
for page in pdf.pages:
page_text = page.extract_text()
if page_text:
text += page_text + "\n"
else:
img = page.to_image(resolution=300).original
ocr_text = pytesseract.image_to_string(img)
text += ocr_text + "\n"
return text
def extract_text_from_docx(file):
doc = docx.Document(file)
return "\n".join([para.text for para in doc.paragraphs if para.text.strip() != ""])
def chunk_text(text, max_chars=6000):
paragraphs = text.split("\n")
chunks, current_chunk = [], ""
for para in paragraphs:
if len(current_chunk) + len(para) < max_chars:
current_chunk += para + "\n"
else:
chunks.append(current_chunk)
current_chunk = para + "\n"
if current_chunk:
chunks.append(current_chunk)
return chunks
# 🧾 Prompt to return only key points
def create_prompt(text_chunk):
return f"""
Extract the following key details from this resume in SHORT key-point format (no long sentences). Return only clean bullet points:
- Name
- Email
- Phone
- Skills (just key skill names or topics)
- Education (just degree, institution, year, no full sentences)
- Experience (just role, company, time period)
- Projects (project title and tech/tools used)
- Certifications (only titles)
CONTENT:
{text_chunk}
Only return bullet points under each section.
"""
def clean_output(raw_output):
start_marker = "Name:"
if start_marker in raw_output:
return raw_output[raw_output.index(start_marker):].strip()
return raw_output.strip()
# 🚀 Main function
@spaces.GPU(duration=60)
def analyze_document(file, cancel_flag):
ext = os.path.splitext(file.name)[-1].lower()
if ext == ".pdf":
raw_text = extract_text_from_pdf(file)
elif ext == ".docx":
raw_text = extract_text_from_docx(file)
else:
return "❌ Unsupported file format", "❌ Invalid format"
if not raw_text.strip():
return "❌ No text found in document", "❌ Empty"
chunks = chunk_text(raw_text)
full_summary = ""
for i, chunk in enumerate(chunks):
if cancel_flag:
return "⛔ Cancelled", "⛔"
prompt = create_prompt(chunk)
result = generator(prompt, max_new_tokens=1024, do_sample=False)[0]["generated_text"]
cleaned = clean_output(result)
full_summary += cleaned + "\n\n---\n\n"
return full_summary.strip(), "✅ Completed"
# 🌐 Interface
with gr.Blocks(title="Smart Resume Parser - Key Points Edition") as demo:
gr.Markdown("## 📄 Resume Parser – Summarized Key Points from PDF/DOCX")
with gr.Row():
with gr.Column(scale=1):
file_input = gr.File(label="📎 Upload Resume (PDF/DOCX)")
with gr.Row():
analyze_button = gr.Button("🔍 Parse", variant="primary")
terminate_button = gr.Button("❌ Cancel", variant="stop")
status_box = gr.Textbox(label="📊 Status", value="⏳ Waiting...", interactive=False)
with gr.Column(scale=2):
output_box = gr.Textbox(label="🧠 Resume Key Highlights", lines=30, interactive=False)
cancel_flag = gr.State(False)
analyze_button.click(
fn=analyze_document,
inputs=[file_input, cancel_flag],
outputs=[output_box, status_box]
)
terminate_button.click(
fn=lambda: gr.update(value=True),
inputs=[],
outputs=[cancel_flag]
)
demo.launch(server_name="0.0.0.0", server_port=7860)
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