import os
import torch
from PIL import Image
from modelscope import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = "./cogvlm2-llama3-chat-19B-int4"
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16


tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_PATH,
    torch_dtype=TORCH_TYPE,
    trust_remote_code=True,
    low_cpu_mem_usage=True,
).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, picking 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:\n"
    "Type: [Fruit/Crop Name]  Growth Stage: [unripe / mature / pest-damaged / rotten]  "
    "Recommendation: [keep for further growth / try to recover it / picking 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(('.jpg', '.jpeg', '.png', '.bmp', '.webp'))]
        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 out_file:
            for filename in image_files:
                image_path = os.path.join(subfolder_path, filename)
                try:
                    print(f"🖼️ Processing {filename}...")

                    image = Image.open(image_path).convert('RGB')
                    history = []

                    input_by_model = model.build_conversation_input_ids(
                        tokenizer,
                        query=question,
                        history=history,
                        images=[image],
                        template_version='chat'
                    )

                    inputs = {
                        'input_ids': input_by_model['input_ids'].unsqueeze(0).to(DEVICE),
                        'token_type_ids': input_by_model['token_type_ids'].unsqueeze(0).to(DEVICE),
                        'attention_mask': input_by_model['attention_mask'].unsqueeze(0).to(DEVICE),
                        'images': [[input_by_model['images'][0].to(DEVICE).to(TORCH_TYPE)]],
                    }

                    gen_kwargs = {
                        "max_new_tokens": 2048,
                        "pad_token_id": 128002,
                    }

                    with torch.no_grad():
                        outputs = model.generate(**inputs, **gen_kwargs)
                        outputs = outputs[:, inputs['input_ids'].shape[1]:]
                        response = tokenizer.decode(outputs[0])
                        response = response.split("<|end_of_text|>")[0].strip()

                    print(f"✅ Response: {response}\n")
                    out_file.write(f"{'='*25} IMAGE START {'='*25}\n")
                    out_file.write(f"🖼️ Image Name: {filename}\n")
                    out_file.write(f"📝 Answer:\n{response}\n")
                    out_file.write(f"{'='*25} IMAGE END {'='*25}\n\n")

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