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import torch
import requests
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
from glob import glob
import json
import os
from datasets import Dataset
def make_chat_data(base="./baseline"):
dataset = []
for filename in sorted(glob(f"{base}/*.jsonl"))[0:1]:
with open(filename, "r") as f:
data = json.loads(f.read())
image_path = f"../Images/{os.path.basename(filename).removesuffix(".jsonl")}.jpg"
image = Image.open(image_path).convert("RGB")
chat = [{
"role": "user",
"content": [
{"type": "text", "text": "You are a professional English-German translator and also a renowned photography critic.\n\nWrite a detailed caption for this image in a single sentence. Translate the caption into German. The output needs to be JSON, the keys being 'English' and 'German' for the respective captions. Only output the JSON, nothing else."},
{"type": "image"}
]
},
#{ "role": "assistant",
# "content": [{"type": "text", "text": '{"English": ' + json.dumps(data["English"]) + ', "German": ' + json.dumps(data["Translation"]) + '}'}]
#}
]
item = {"image": image, "chat": chat}
dataset.append(item)
return Dataset.from_list(dataset)
model_id = "google/gemma-3-4b-it"
processor = AutoProcessor.from_pretrained(model_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
dtype=torch.bfloat16,
device_map="auto",
attn_implementation="eager",
)
device = model.device
dataset = make_chat_data()
chat_prompt = processor.tokenizer.apply_chat_template(
[item["chat"] for item in dataset],
tokenize=False,
add_generation_prompt=True,
)
print(dataset[0])
inputs = processor(
text=chat_prompt,
images=[item["image"] for item in dataset],
return_tensors="pt"
).to(device)
print("Keys in the output:", inputs.keys())
input_ids = inputs["input_ids"]
print("\nShape of input_ids:", input_ids.shape)
print("input_ids:", input_ids)
decoded_text = processor.decode(input_ids[0], skip_special_tokens=False)
print("\nDecoded input_ids (showing special tokens):")
print(decoded_text)
pixel_values = inputs["pixel_values"]
print("\n--- Generating a Response ---")
output = model.generate(
**inputs,
max_new_tokens=100
)
generated_text = processor.decode(output[0], skip_special_tokens=True)
print("\nModel's response:\n", generated_text)
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