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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel
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"
model_name = "THUDM/glm-4-9b"
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
file_input = gr.File(label="Upload TXT or PDF Document", file_count="single")
prompt_input = gr.Textbox(label="Prompt", placeholder="Enter your structured prompt here")
output_text = gr.Textbox(label="Analysis Result")
iface = gr.Interface(
fn=analyze_document,
inputs=[file_input, prompt_input],
outputs=output_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() |