import gradio as gr from transformers import AutoModelForCausalLM, AutoTokenizer import fitz # PyMuPDF for PDF handling # Function to extract text from PDF def extract_text_from_pdf(pdf_path): doc = fitz.open(pdf_path) text = "" for page in doc: text += page.get_text() return text # Function to handle file upload and text input def analyze_document(file, prompt): # Check file type and extract text accordingly if file.name.endswith(".pdf"): text = extract_text_from_pdf(file.name) elif file.name.endswith(".txt"): text = file.read().decode("utf-8") else: return "Unsupported file format. Please upload a PDF or TXT file." # Load model and tokenizer model_name = "Alibaba-NLP/gte-Qwen1.5-7B-instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) # Generate input for the model input_text = f"Document content:\n{text}\n\nPrompt:\n{prompt}" inputs = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**inputs) response = tokenizer.decode(outputs[0], skip_special_tokens=True) return response # Define Gradio interface iface = gr.Interface( fn=analyze_document, inputs=[ gr.inputs.File(label="Upload TXT or PDF Document"), gr.inputs.Textbox(label="Prompt", placeholder="Enter your structured prompt here") ], outputs="text", title="Document Analysis with GPT Model", description="Upload a TXT or PDF document and enter a prompt to get an analysis." ) # Launch the interface iface.launch()