Spaces:
Running
Running
File size: 2,492 Bytes
5a7d5c7 26435ba 9af5fdb 26435ba 5a7d5c7 26435ba 5a7d5c7 9af5fdb 26435ba 9af5fdb 26435ba 5a7d5c7 6d798ab 26435ba 5a7d5c7 26435ba 5a7d5c7 26435ba 5a7d5c7 26435ba 5a7d5c7 26435ba 5a7d5c7 26435ba 5a7d5c7 26435ba 5a7d5c7 26435ba 5a7d5c7 9af5fdb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
import gradio as gr
from transformers import AutoProcessor, AutoModelForCausalLM
from PIL import Image
import torch
from fastapi import FastAPI
from fastapi.responses import RedirectResponse
# Initialize FastAPI
app = FastAPI()
# Load models - Using microsoft/git-large-coco
try:
# Load the better model
processor = AutoProcessor.from_pretrained("microsoft/git-large-coco")
git_model = AutoModelForCausalLM.from_pretrained("microsoft/git-large-coco")
print("Successfully loaded microsoft/git-large-coco model")
USE_GIT = True
except Exception as e:
print(f"Failed to load GIT model: {e}. Falling back to smaller model")
captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
USE_GIT = False
def generate_caption(image_path):
"""Generate caption using the best available model"""
try:
if USE_GIT:
image = Image.open(image_path)
inputs = processor(images=image, return_tensors="pt")
outputs = git_model.generate(**inputs, max_length=50)
return processor.batch_decode(outputs, skip_special_tokens=True)[0]
else:
result = captioner(image_path)
return result[0]['generated_text']
except Exception as e:
print(f"Caption generation error: {e}")
return "Could not generate caption"
def process_image(file_path: str):
"""Handle image processing for Gradio interface"""
if not file_path:
return "Please upload an image first"
try:
caption = generate_caption(file_path)
return f"📷 Image Caption:\n{caption}"
except Exception as e:
return f"Error processing image: {str(e)}"
# Gradio Interface
with gr.Blocks(title="Image Captioning Service", theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🖼️ Image Captioning Service")
gr.Markdown("Upload an image to get automatic captioning")
with gr.Row():
with gr.Column():
image_input = gr.Image(label="Upload Image", type="filepath")
analyze_btn = gr.Button("Generate Caption", variant="primary")
with gr.Column():
output = gr.Textbox(label="Caption Result", lines=5)
analyze_btn.click(
fn=process_image,
inputs=[image_input],
outputs=[output]
)
# Mount Gradio app to FastAPI
app = gr.mount_gradio_app(app, demo, path="/")
@app.get("/")
def redirect_to_interface():
return RedirectResponse(url="/")
|