app.py

app.py

@@ -1,64 +1,59 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import pytesseract
+from PIL import Image
+import requests
+import re
+import traceback
+import os
+
+# 配置 Tesseract OCR 的路径（Hugging Face Spaces 自动配置）
+pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'
+
+# 使用环境变量获取 Hugging Face API Token
+API_URL = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Math-72B-Instruct"
+API_TOKEN = os.getenv("HF_API_TOKEN")  # 从环境变量获取 Token
+HEADERS = {"Authorization": f"Bearer {API_TOKEN}"}
+
+# OCR 识别函数
+def ocr_with_tesseract(image_path):
+    try:
+        image = Image.open(image_path).convert("L")
+        config = "--psm 6"
+        text = pytesseract.image_to_string(image, config=config)
+        text = re.sub(r'[^0-9a-zA-Z=+\-*/()., ]', '', text)
+        return text if text else "OCR 识别失败"
+    except Exception as e:
+        return f"OCR 识别错误: {e}\n{traceback.format_exc()}"
+
+# AI 解答生成函数
+def generate_solution_with_qwen(question):
+    prompt = f"请详细解答以下数学题目：{question}"
+    payload = {"inputs": prompt}
+    response = requests.post(API_URL, headers=HEADERS, json=payload)
+    
+    if response.status_code == 200:
+        result = response.json()
+        return result.get('generated_text', "解答生成失败")
+    else:
+        return f"API 调用失败，状态码: {response.status_code}, 响应: {response.text}"
+
+# 主处理函数
+def process(image_path):
+    ocr_result = ocr_with_tesseract(image_path)
+    ai_solution = generate_solution_with_qwen(ocr_result)
+    return ocr_result, ai_solution
+
+# 构建 Gradio 应用界面
+def build_interface():
+    with gr.Blocks() as interface:
+        gr.Markdown("# 📚 高级 AI 数学解题助手")
+        image_input = gr.Image(type="filepath", label="上传数学题目图片")
+        ocr_output = gr.Textbox(label="OCR 识别结果")
+        ai_output = gr.Markdown(label="AI 解答")
+        submit_button = gr.Button("识别并解答")
+        submit_button.click(fn=process, inputs=image_input, outputs=[ocr_output, ai_output])
+    return interface
+
+# 启动 Gradio 应用
+interface = build_interface()
+interface.launch()