app.py

import os
import gradio as gr
import json
from rxnim import RXNIM
from getReaction import generate_combined_image
import torch
from rxn.reaction import Reaction

PROMPT_DIR = "prompts/"
ckpt_path = "./rxn/model/model.ckpt"
model = Reaction(ckpt_path, device=torch.device('cpu'))

# 定义 prompt 文件名到友好名字的映射
PROMPT_NAMES = {
    "2_RxnOCR.txt": "Reaction Image Parsing Workflow",
}
example_diagram = "examples/exp.png"

def list_prompt_files_with_names():
    """
    列出 prompts 目录下的所有 .txt 文件，为没有名字的生成默认名字。
    返回 {friendly_name: filename} 映射。
    """
    prompt_files = {}
    for f in os.listdir(PROMPT_DIR):
        if f.endswith(".txt"):
            # 如果文件名有预定义的名字，使用预定义名字
            friendly_name = PROMPT_NAMES.get(f, f"Task: {os.path.splitext(f)[0]}")
            prompt_files[friendly_name] = f
    return prompt_files

def parse_reactions(output_json):
    """
    解析 JSON 格式的反应数据并格式化输出，包含颜色定制。
    """
    reactions_data = json.loads(output_json)  # 转换 JSON 字符串为字典
    reactions_list = reactions_data.get("reactions", [])
    detailed_output = []

    for reaction in reactions_list:
        reaction_id = reaction.get("reaction_id", "Unknown ID")
        reactants = [r.get("smiles", "Unknown") for r in reaction.get("reactants", [])]
        conditions = [
            f"<span style='color:red'>{c.get('smiles', c.get('text', 'Unknown'))}[{c.get('role', 'Unknown')}]</span>"
            for c in reaction.get("conditions", [])
        ]
        conditions_1 = [
            f"<span style='color:black'>{c.get('smiles', c.get('text', 'Unknown'))}[{c.get('role', 'Unknown')}]</span>"
            for c in reaction.get("conditions", [])
        ]
        products = [f"<span style='color:orange'>{p.get('smiles', 'Unknown')}</span>" for p in reaction.get("products", [])]
        products_1 = [f"<span style='color:black'>{p.get('smiles', 'Unknown')}</span>" for p in reaction.get("products", [])]

        # 构造反应的完整字符串，定制字体颜色
        full_reaction = f"{'.'.join(reactants)}>>{'.'.join(products_1)} | {', '.join(conditions_1)}"
        full_reaction = f"<span style='color:black'>{full_reaction}</span>"
        
        # 详细反应格式化输出
        reaction_output = f"<b>Reaction: </b> {reaction_id}<br>"
        reaction_output += f"  Reactants: <span style='color:blue'>{', '.join(reactants)}</span><br>"
        reaction_output += f"  Conditions: {', '.join(conditions)}<br>"
        reaction_output += f"  Products: {', '.join(products)}<br>"
        reaction_output += f"  <b>Full Reaction:</b> {full_reaction}<br>"
        reaction_output += "<br>"
        detailed_output.append(reaction_output)

    return detailed_output

def process_chem_image(image, selected_task):
    chem_mllm = RXNIM()

    # 将友好名字转换为实际文件名
    prompt_path = os.path.join(PROMPT_DIR, prompts_with_names[selected_task])
    image_path = "temp_image.png"
    image.save(image_path)

    # 调用 RXNIM 处理
    rxnim_result = chem_mllm.process(image_path, prompt_path)

    # 将 JSON 结果解析为结构化输出
    detailed_reactions = parse_reactions(rxnim_result)

    # 调用 RxnScribe 模型处理并生成整合图像
    predictions = model.predict_image_file(image_path, molscribe=True, ocr=True)
    combined_image_path = generate_combined_image(predictions, image_path)

    json_file_path = "output.json"
    with open(json_file_path, "w") as json_file:
        json.dump(json.loads(rxnim_result), json_file, indent=4)


    # 返回详细反应和整合图像
    return "\n\n".join(detailed_reactions), combined_image_path, example_diagram, json_file_path


# 获取 prompts 和友好名字
prompts_with_names = list_prompt_files_with_names()

# 示例数据：图像路径 + 任务选项
examples = [
    
    ["examples/reaction1.png", "Reaction Image Parsing Workflow"],
    ["examples/reaction2.png", "Reaction Image Parsing Workflow"],
    ["examples/reaction3.png", "Reaction Image Parsing Workflow"],
    ["examples/reaction4.png", "Reaction Image Parsing Workflow"],
]

# 定义 Gradio 界面
demo = gr.Interface(
    fn=process_chem_image,
    inputs=[
        gr.Image(type="pil", label="Upload Reaction Image"),
        gr.Radio(
            choices=list(prompts_with_names.keys()),  # 显示任务名字
            label="Select a predefined task",
        ),
    ],
    outputs=[
        gr.HTML(label="Reaction outputs"),
        gr.Image(label="Visualization"), # 显示整合图像
        gr.Image(value=example_diagram, label="Schematic Diagram"),
        gr.File(label="Download JSON File"),

    ],
    title="Towards Large-scale Chemical Reaction Image Parsing via a Multimodal Large Language Model",
    description="Upload a reaction image and select a predefined task prompt.",
    examples=examples,  # 使用嵌套列表作为示例
    examples_per_page=20,
)

demo.launch()