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import gradio as gr
from inference import generate_rag_caption
def predict(image):
"""
Wrapper for Gradio to generate a caption from an uploaded image.
Input: PIL Image
Output: Caption (str)
"""
caption = generate_rag_caption(image)
return caption
# Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil", label="Upload an Image"),
outputs=gr.Textbox(label="Generated Caption"),
title="RAG Image Captioning",
description="Upload an image to generate a caption using a RAG-based pipeline with CLIP, T5, and SentenceTransformer."
)
if __name__ == "__main__":
iface.launch(share=True,ssr_mode=False)
# FastAPI app
app = FastAPI()
# REST API endpoint for image captioning
@app.post("/caption-image")
async def caption_image(file: UploadFile = File(...)):
contents = await file.read()
image = Image.open(io.BytesIO(contents)).convert("RGB")
caption = generate_rag_caption(image)
return {"caption": caption}
# Mount Gradio at root "/"
app = gr.mount_gradio_app(app, iface, path="/")
# import gradio as gr
# from inference import generate_rag_caption
# from fastapi import FastAPI, File, UploadFile
# from PIL import Image
# import io
# # Function for Gradio
# def predict(image):
# caption = generate_rag_caption(image)
# return caption
# # Gradio interface
# iface = gr.Interface(
# fn=predict,
# inputs=gr.Image(type="pil", label="Upload an Image"),
# outputs=gr.Textbox(label="Generated Caption"),
# title="RAG Image Captioning",
# description="Upload an image to generate a caption using a RAG-based pipeline with CLIP, T5, and SentenceTransformer."
# )
# # FastAPI app
# app = FastAPI()
# # REST API endpoint for image captioning
# @app.post("/caption-image")
# async def caption_image(file: UploadFile = File(...)):
# contents = await file.read()
# image = Image.open(io.BytesIO(contents)).convert("RGB")
# caption = generate_rag_caption(image)
# return {"caption": caption}
# # Mount Gradio at root "/"
# app = gr.mount_gradio_app(app, iface, path="/")
# import gradio as gr
# from inference import generate_rag_caption
# from fastapi import FastAPI, File, UploadFile
# from PIL import Image
# import io
# # Step 1: Create FastAPI app
# app = FastAPI()
# # Step 2: Add an API route that accepts image uploads
# @app.post("/caption-image")
# async def caption_image(file: UploadFile = File(...)):
# contents = await file.read()
# image = Image.open(io.BytesIO(contents)).convert("RGB")
# caption = generate_rag_caption(image)
# return {"caption": caption}
# # Step 3: Create the original Gradio interface
# gradio_interface = gr.Interface(
# fn=generate_rag_caption,
# inputs=gr.Image(type="pil", label="Upload an Image"),
# outputs=gr.Textbox(label="Generated Caption"),
# title="RAG Image Captioning",
# description="Upload an image to generate a caption using a RAG-based pipeline with CLIP, T5, and SentenceTransformer."
# )
# # ✅ Step 4: Mount Gradio interface on root path `/`
# app = gr.mount_gradio_app(app, gradio_interface, path="/") |