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import gradio as gr
from huggingface_hub import InferenceClient
import PyPDF2
import docx
import io
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def extract_text_from_pdf(pdf_file):
pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_file))
text = ""
for page in pdf_reader.pages:
text += page.extract_text() + "\n"
return text
def extract_text_from_docx(docx_file):
doc = docx.Document(io.BytesIO(docx_file))
return "\n".join([para.text for para in doc.paragraphs])
def parse_cv(file):
if file is None:
return "Please upload a CV file."
file_ext = file.name.split(".")[-1].lower()
file_bytes = file.read()
if file_ext == "pdf":
text = extract_text_from_pdf(file_bytes)
elif file_ext == "docx":
text = extract_text_from_docx(file_bytes)
else:
return "Unsupported file format. Please upload a PDF or DOCX file."
prompt = f"Analyze the following CV and generate a professional summary and improvement suggestions:\n\n{text}"
response = client.text_generation(prompt, max_tokens=512)
return response
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
demo = gr.Blocks()
with demo:
gr.Markdown("## AI-powered CV Analyzer and Chatbot")
with gr.Tab("Chatbot"):
chat_interface = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
with gr.Tab("CV Analyzer"):
gr.Markdown("### Upload your CV (PDF or DOCX) to receive a professional analysis.")
file_input = gr.File(label="Upload CV", type="file")
output_text = gr.Textbox(label="CV Analysis Report", lines=10)
analyze_button = gr.Button("Analyze CV")
analyze_button.click(parse_cv, inputs=file_input, outputs=output_text)
if __name__ == "__main__":
demo.launch()
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