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import gradio as gr | |
import PyPDF2 | |
import pytesseract | |
from PIL import Image | |
# Ensure Tesseract is installed and accessible | |
# On Windows, you may need to specify the Tesseract path: | |
# pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe' | |
def process_file(file): | |
if file is None: | |
return "No file uploaded." | |
content = "" | |
if file.name.endswith(".txt"): | |
# Read text files | |
with open(file.name, "r") as f: | |
content = f.read() | |
elif file.name.endswith(".pdf"): | |
# Extract text from PDFs | |
reader = PyPDF2.PdfReader(file.name) | |
for page in reader.pages: | |
content += page.extract_text() | |
elif file.name.endswith((".png", ".jpg", ".jpeg")): | |
# Extract text from images using OCR | |
image = Image.open(file.name) | |
content = pytesseract.image_to_string(image) | |
else: | |
return f"Unsupported file type: {file.name}" | |
# Simulate passing the content to the phi-4 model | |
model_response = f"Processed file content:\n{content}" | |
return model_response | |
with gr.Blocks(fill_height=True) as demo: | |
with gr.Sidebar(): | |
gr.Markdown("# Inference Provider") | |
gr.Markdown("This Space showcases the microsoft/phi-4 model, served by the nebius API. Sign in with your Hugging Face account to use this API.") | |
button = gr.LoginButton("Sign in") | |
with gr.Column(): | |
# Load the phi-4 model | |
model = gr.load("models/microsoft/phi-4", accept_token=button, provider="nebius") | |
# File upload component | |
file_input = gr.File(label="Upload a file (TXT, PDF, or Image)") | |
# Output component to display model response | |
file_output = gr.Textbox(label="Model Response", lines=10) | |
# Connect the file upload to the processing function | |
file_input.change(process_file, inputs=file_input, outputs=file_output) | |
demo.launch() |