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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() | |