app.py

import os
import torch
from unsloth import FastVisionModel
from peft import PeftModel
from transformers import AutoProcessor
from PIL import Image
import gradio as gr

# Load base LLaMA vision model
model_name = "unsloth/Llama-3.2-11B-Vision-Instruct"
lora_repo = "alinasdkey/unsloth-pret-lora"

# Load base model and processor
model, processor = FastVisionModel.from_pretrained(
    model_name=model_name,
    device_map="auto",
    load_in_4bit=False,
    load_in_8bit=True,
)

# Apply LoRA adapter
model = PeftModel.from_pretrained(model, model_id=lora_repo)

# Set to inference mode
FastVisionModel.for_inference(model)

# Inference function
def describe_image(image, instruction):
    # Load and preprocess image
    image = image.convert("RGB")
    inputs = processor(images=image, return_tensors="pt").to(model.device)

    # Create input prompt with instruction
    prompt = instruction if instruction else "Describe this graph."

    # Tokenize text prompt
    input_ids = processor.tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)

    # Extract necessary vision inputs
    pixel_values = inputs["pixel_values"]
    aspect_ratio_ids = inputs.get("aspect_ratio_ids")
    aspect_ratio_mask = inputs.get("aspect_ratio_mask")

    # Generate model output
    outputs = model.generate(
        input_ids=input_ids,
        pixel_values=pixel_values,
        aspect_ratio_ids=aspect_ratio_ids,
        aspect_ratio_mask=aspect_ratio_mask,
        max_new_tokens=256,
        do_sample=False,
        temperature=0.2,
        top_p=0.95,
    )

    # Decode and return result
    return processor.tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

# Gradio Interface
gr.Interface(
    fn=describe_image,
    inputs=[
        gr.Image(type="pil", label="Upload a Graph Image"),
        gr.Textbox(label="Instruction (e.g. Summarize this graph)")
    ],
    outputs="text",
    title="Welcome to the Graph Description AI: Pret",
    description="Upload a graph and get insightful analysis!"
).launch()