Datasets:

TJIET
/

FruitBench

@@ -1,110 +0,0 @@
-import os
-import torch
-from tqdm import tqdm
-from transformers import AutoModelForCausalLM
-from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
-from deepseek_vl.utils.io import load_pil_images
-from PIL import Image
-model_path = "deepseek-ai/deepseek-vl-7b-chat"
-vl_chat_processor: VLChatProcessor = VLChatProcessor.from_pretrained(model_path)
-tokenizer = vl_chat_processor.tokenizer
-vl_gpt: MultiModalityCausalLM = AutoModelForCausalLM.from_pretrained(
-    model_path,
-    cache_dir=".",
-    trust_remote_code=True
-)
-vl_gpt = vl_gpt.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:\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'))]
-        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}"):
-                image_path = os.path.join(subfolder_path, image_file)
-                try:
-                    conversation = [
-                        {
-                            "role": "User",
-                            "content": "<image_placeholder>" + 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).strip()
-                    fout.write(f"{'=' * 25} IMAGE START {'=' * 25}\n")
-                    fout.write(f"🖼️ Image Name: {image_file}\n")
-                    fout.write(f"📝 Answer:\n{answer}\n")
-                    fout.write(f"{'=' * 25} IMAGE END {'=' * 25}\n\n")
-                    print(f"✅ {image_file} => {answer.splitlines()[0]}")
-                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")