import os
import torch
from transformers import AutoModelForCausalLM
from janus.models import MultiModalityCausalLM, VLChatProcessor
from janus.utils.io import load_pil_images
from tqdm import tqdm

#https://github.com/deepseek-ai/Janus


model_path = "deepseek-ai/Janus-Pro-7B"
vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path, cache_dir=".")
tokenizer = vl_chat_processor.tokenizer
vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
    model_path, cache_dir=".", trust_remote_code=True
).to(torch.bfloat16).cuda().eval()


question = (
'''
1. Identify the type of fruit or crop shown in the image.  
2. Determine its current growth stage. (Options: unripe, mature, pest-damaged, rotten)  
3. Recommend the farmer’s next action. (Options: keep for further growth, try to recover it, discard it)  
4. Evaluate the consumer’s willingness to consume this fruit, from 1 (very unlikely) to 100 (very likely).
Please respond in the following format:
Type: [Fruit/Crop Name]  Growth Stage: [unripe / mature / pest-damaged / rotten]  Recommendation: [keep for further growth / pick it /  try to recover it / discard it]  Consumer Score: [1-100]
 '''
)

root_folder = "../data"
output_root = "result"
os.makedirs(output_root, exist_ok=True)


for fruit in os.listdir(root_folder):
    fruit_path = os.path.join(root_folder, fruit)
    if not os.path.isdir(fruit_path):
        continue

    for subfolder in os.listdir(fruit_path):
        subfolder_path = os.path.join(fruit_path, subfolder)
        if not os.path.isdir(subfolder_path):
            continue

        image_files = [f for f in os.listdir(subfolder_path) if f.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp'))]
        if not image_files:
            continue

        output_file = os.path.join(output_root, f"{fruit}_{subfolder}.txt")
        
        with open(output_file, "w", encoding="utf-8") as fout:
            for image_file in tqdm(image_files, desc=f"{fruit}/{subfolder}"):
                try:
                    image_path = os.path.join(subfolder_path, image_file)

                   
                    conversation = [
                        {
                            "role": "<|User|>",
                            "content": f"<image_placeholder>\n{question}",
                            "images": [image_path],
                        },
                        {"role": "<|Assistant|>", "content": ""},
                    ]

                    pil_images = load_pil_images(conversation)
                    prepare_inputs = vl_chat_processor(
                        conversations=conversation,
                        images=pil_images,
                        force_batchify=True
                    ).to(vl_gpt.device)

                   
                    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)

                    outputs = vl_gpt.language_model.generate(
                        inputs_embeds=inputs_embeds,
                        attention_mask=prepare_inputs.attention_mask,
                        pad_token_id=tokenizer.eos_token_id,
                        bos_token_id=tokenizer.bos_token_id,
                        eos_token_id=tokenizer.eos_token_id,
                        max_new_tokens=512,
                        do_sample=False,
                        use_cache=True,
                    )

                    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
                    fout.write(f"{'='*25} IMAGE START {'='*25}\n")
                    fout.write(f"🖼️ Image Name: {image_file}\n")
                    fout.write(f"📝 Answer:\n{answer.strip()}\n")
                    fout.write(f"{'='*25} IMAGE END {'='*25}\n\n")

                except Exception as e:
                    print(f"[ERROR] {fruit}/{subfolder}/{image_file}: {e}")
                    fout.write(f"{'='*25} IMAGE START {'='*25}\n")
                    fout.write(f"🖼️ Image Name: {image_file}\n")
                    fout.write(f"❌ ERROR: {e}\n")
                    fout.write(f"{'='*25} IMAGE END {'='*25}\n\n")