app.py

import gradio as gr
from transformers import BlipProcessor, BlipForConditionalGeneration
from huggingface_hub import login
from PIL import Image
 
# Step 1: Authenticate with Hugging Face using your token
login(token="")  # Paste your token here
 
# Step 2: Load the processor and the private model
model_name = "anushettypsl/paligemma_vqav2"  # Replace with actual model link
processor = BlipProcessor.from_pretrained(model_name)
model = BlipForConditionalGeneration.from_pretrained(model_name)
 
# Step 3: Define the prediction function
def predict(image):
    inputs = processor(image, return_tensors="pt")
    outputs = model.generate(**inputs)
    generated_text = processor.decode(outputs[0], skip_special_tokens=True)
    return generated_text
 
# Step 4: Create the Gradio interface
interface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),  # Image input
    outputs="text",               # Text output
    title="Image-to-Text Model"
)
 
# Step 5: Launch the app
interface.launch()