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from flask import Flask, request, jsonify, render_template, Response |
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import os |
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from dotenv import load_dotenv |
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import json |
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import requests |
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from PIL import Image |
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import base64 |
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from io import BytesIO |
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import logging |
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import time |
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load_dotenv() |
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app = Flask(__name__) |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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def create_headers(): |
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"""创建API请求头""" |
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return { |
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"Content-Type": "application/json", |
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"Authorization": f"Bearer {os.getenv('OPENAI_API_KEY')}" |
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} |
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def image_to_base64(image): |
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"""将PIL图像转换为base64""" |
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buffered = BytesIO() |
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image.save(buffered, format="PNG") |
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return base64.b64encode(buffered.getvalue()).decode() |
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def process_image_with_vision(image_base64): |
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"""使用Vision API处理图像""" |
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url = f"{os.getenv('OPENAI_API_BASE')}/v1/chat/completions" |
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messages = [ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "text", |
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"text": """请仔细分析这张图片中的题目,要求: |
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1. 识别内容类型: |
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- 判断是否为数学题、物理题、化学题等 |
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- 识别是选择题、填空题还是解答题 |
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- 判断是否包含图表或特殊符号 |
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2. 内容提取: |
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- 提取所有文字内容,包括题干、选项(如果有) |
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- 识别所有数学公式、化学方程式或特殊符号 |
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- 保留原有的排版格式(如换行、缩进等) |
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3. 公式处理: |
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- 将所有数学公式转换为LaTeX格式 |
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- 使用[formula_n]作为占位符,其中n为公式编号 |
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- 保持公式的完整性和准确性 |
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请使用以下JSON格式返回: |
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{ |
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"type": "题目类型(如:数学/物理/化学)", |
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"format": "题目格式(如:选择题/填空题/解答题)", |
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"text": "包含[formula_1], [formula_2]等占位符的完整文本", |
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"formulas": ["latex公式1", "latex公式2"], |
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"options": ["A. xxx", "B. xxx"] // 如果是选择题则包含此字段 |
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"notes": ["可能存在的问题说明1", "问题说明2"] |
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}""" |
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}, |
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{ |
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"type": "image_url", |
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"image_url": { |
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"url": f"data:image/png;base64,{image_base64}" |
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} |
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} |
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] |
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} |
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] |
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payload = { |
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"model": os.getenv('OPENAI_VISION_MODEL'), |
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"messages": messages, |
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"max_tokens": 1500, |
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"temperature": 0.2 |
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} |
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try: |
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response = requests.post(url, headers=create_headers(), json=payload) |
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response.raise_for_status() |
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result = response.json() |
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content = result['choices'][0]['message']['content'] |
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try: |
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return json.loads(content) |
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except json.JSONDecodeError: |
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return { |
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"type": "unknown", |
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"format": "unknown", |
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"text": content, |
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"formulas": [], |
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"notes": ["无法解析为JSON格式"] |
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} |
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except Exception as e: |
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logger.error(f"Vision API调用错误: {str(e)}") |
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return {"error": str(e)} |
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def handle_sse_response(raw_data): |
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"""处理SSE响应数据""" |
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if raw_data: |
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try: |
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data = json.loads(raw_data) |
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if len(data['choices']) > 0: |
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delta = data['choices'][0].get('delta', {}) |
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content = delta.get('content', '') |
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print(f"Stream content: {content}", flush=True) |
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return content |
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except json.JSONDecodeError: |
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pass |
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return '' |
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def stream_solve(problem_text, formulas): |
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url = f"{os.getenv('OPENAI_API_BASE')}/v1/chat/completions" |
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full_problem = problem_text |
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for i, formula in enumerate(formulas, 1): |
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full_problem = full_problem.replace(f"[formula_{i}]", f"$${formula}$$") |
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print(f"\n问题内容: {full_problem}") |
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messages = [ |
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{ |
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"role": "system", |
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"content": """请按照以下格式回答问题: |
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[开始解答] |
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这是答案:(写出明确的答案,如"答案为:2.5米","答案选 A"等) |
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[解答结束] |
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[开始解析] |
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这是解析:(写出详细的解析过程,需严格遵循以下要求: |
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解题思路要求: |
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1. 开篇明确指出题目涉及的核心知识点和解题方向 |
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2. 采用合理的解题策略,并说明策略选择的依据 |
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3. 对于多解法题目,说明最优解法的选择理由 |
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专业性要求: |
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1. 严格使用规范的学科专业术语,避免口语化表达 |
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2. 数学公式、物理量、化学方程式等必须符合学科规范 |
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3. 计算步骤要详实,重要的中间步骤不得省略 |
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4. 注意数据的有效位数,物理量单位的规范性 |
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逻辑性要求: |
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1. 解题步骤要层次分明,逻辑推导严谨 |
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2. 关键结论要有充分的推导过程和理论依据 |
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3. 复杂问题应合理拆分为子步骤,循序渐进 |
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教学性要求: |
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1. 对重要概念和关键步骤要适当添加解释说明 |
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2. 特别标注解题中的难点和易错点 |
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3. 适时补充相关的知识点拓展 |
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4. 针对典型错误进行分析和提醒 |
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书写规范: |
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1. 专业符号和公式要规范,确保清晰美观 |
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2. 合理使用缩进和分段,突出层次结构 |
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3. 保持语言的严谨性和专业性) |
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[解析结束]""" |
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}, |
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{ |
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"role": "user", |
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"content": full_problem |
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} |
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] |
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payload = { |
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"model": os.getenv('OPENAI_CHAT_MODEL'), |
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"messages": messages, |
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"stream": True, |
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"temperature": 0.3 |
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} |
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try: |
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response = requests.post( |
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url, |
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headers=create_headers(), |
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json=payload, |
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stream=True |
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) |
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response.raise_for_status() |
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complete_response = "" |
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for line in response.iter_lines(): |
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if not line or not line.startswith(b'data: '): |
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continue |
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try: |
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data = json.loads(line[6:].decode('utf-8')) |
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content = data['choices'][0].get('delta', {}).get('content', '') |
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complete_response += content |
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except json.JSONDecodeError: |
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continue |
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except Exception as e: |
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print(f"\n错误: {str(e)}") |
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yield f"data: {json.dumps({'error': str(e)})}\n\n" |
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return |
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try: |
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if '[开始解答]' in complete_response and '[开始解析]' in complete_response: |
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parts = complete_response.split('[开始解答]') |
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answer_part = parts[1].split('[解答结束]')[0].strip() |
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parts = complete_response.split('[开始解析]') |
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analysis_part = parts[1].split('[解析结束]')[0].strip() |
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print("\n完整回答:") |
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print(f"答案: {answer_part}") |
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print(f"解析: {analysis_part}") |
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yield f"data: {json.dumps({'type': 'answer', 'content': answer_part})}\n\n" |
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yield f"data: {json.dumps({'type': 'analysis', 'content': analysis_part})}\n\n" |
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else: |
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print("\n响应格式不符合预期") |
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yield f"data: {json.dumps({'error': '响应格式不符合预期'})}\n\n" |
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except Exception as e: |
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print(f"\n解析错误: {str(e)}") |
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yield f"data: {json.dumps({'error': f'解析响应时出错: {str(e)}'})}\n\n" |
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@app.route('/') |
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def index(): |
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return render_template('index.html') |
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@app.route('/process', methods=['POST']) |
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def process(): |
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if 'file' not in request.files: |
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return jsonify({'error': '没有文件上传'}), 400 |
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file = request.files['file'] |
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if file.filename == '': |
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return jsonify({'error': '没有选择文件'}), 400 |
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try: |
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image = Image.open(file) |
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image_base64 = image_to_base64(image) |
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result = process_image_with_vision(image_base64) |
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if 'error' in result: |
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return jsonify({'error': result['error']}), 500 |
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return jsonify({ |
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'original_image': image_base64, |
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'result': result |
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}) |
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except Exception as e: |
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logger.error(f"处理图像错误: {str(e)}") |
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return jsonify({'error': str(e)}), 500 |
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@app.route('/solve', methods=['POST']) |
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def solve(): |
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data = request.json |
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if not data or 'text' not in data or 'formulas' not in data: |
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return jsonify({'error': '无效的请求数据'}), 400 |
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return Response( |
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stream_solve(data['text'], data['formulas']), |
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content_type='text/event-stream' |
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) |
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if __name__ == '__main__': |
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app.run(host='0.0.0.0', port=7860, debug=True) |