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import gradio as gr
from PIL import Image
from transformers import pipeline, AutoModelForVision2Seq, AutoProcessor
import torch
# Load the OpenGVLab/InternVL-Chat-V1-5 model and processor
processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL-Chat-V1-5")
model = AutoModelForVision2Seq.from_pretrained("OpenGVLab/InternVL-Chat-V1-5")
# Load the Llama3 model for text processing
llama_model = pipeline("text2text-generation", model="llama3")
def process_image(image):
# Process the image to extract the recipe using OpenGVLab
inputs = processor(images=image, return_tensors="pt")
generated_ids = model.generate(**inputs)
extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
return extracted_text
def adjust_recipe(extracted_text, adjustment):
# Create the prompt for Llama3 to adjust the recipe
prompt = f"Here is a recipe: {extracted_text}. Please {adjustment} the recipe."
response = llama_model(prompt)
return response[0]['generated_text']
def app(image, adjustment):
extracted_text = process_image(image)
adjusted_recipe = adjust_recipe(extracted_text, adjustment)
return adjusted_recipe
# Create the Gradio interface
interface = gr.Interface(
fn=app,
inputs=[gr.inputs.Image(type="pil"), gr.inputs.Dropdown(["double", "halve"])],
outputs="text",
title="Recipe Adjuster",
description="Upload an image of a recipe, and this app will double or halve the recipe."
)
if __name__ == "__main__":
interface.launch()
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