Create app.py

app.py

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+# app.py
+!pip install gradio
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    AutoModelForTokenClassification,
+    pipeline
+)
+import gradio as gr
+import torch
+
+
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+
+
+CLASS_MODEL_NAME = "AmandaCAI/resume-classifier"  
+NER_MODEL_NAME = "AmandaCAI/ner-keywords-extract"     
+
+# 初始化分类模型
+class_tokenizer = AutoTokenizer.from_pretrained(CLASS_MODEL_NAME)
+class_model = AutoModelForSequenceClassification.from_pretrained(CLASS_MODEL_NAME)
+
+# 初始化NER模型
+ner_tokenizer = AutoTokenizer.from_pretrained(NER_MODEL_NAME)
+ner_model = AutoModelForTokenClassification.from_pretrained(NER_MODEL_NAME)
+ner_pipeline = pipeline("ner", model=ner_model, tokenizer=ner_tokenizer, aggregation_strategy="simple")
+
+# 岗位分类标签（根据你的训练数据调整）
+job_categories = [
+    "Data Science", "Java Developer", "HR", 
+    "Python Developer", "Web Designing", "Testing"
+]
+
+def analyze_resume(text):
+    """处理简历分析的主函数"""
+    # 岗位分类
+    class_inputs = class_tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    with torch.no_grad():
+        class_logits = class_model(**class_inputs).logits
+    predicted_class = torch.argmax(class_logits).item()
+    class_label = job_categories[predicted_class]
+    
+    # 技能提取
+    ner_results = ner_pipeline(text)
+    skills = [entity["word"] for entity in ner_results if entity["entity_group"] == "SKILL"]
+    
+    # 工作经验提取（示例）
+    experience = [entity["word"] for entity in ner_results if entity["entity_group"] == "EXPERIENCE"]
+    
+    return {
+        "岗位类别": class_label,
+        "匹配度": f"{torch.softmax(class_logits, dim=1)[0][predicted_class].item()*100:.1f}%",
+        "核心技能": list(set(skills))[:5],  # 取前5个不重复技能
+        "工作经验": list(set(experience))[:3]
+    }
+
+# Gradio界面设计
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🧠 AI Resume Analyzer")
+    
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="📝 Paste Resume Text Here", lines=10, 
+                                  placeholder="Enter your resume text here...")
+            submit_btn = gr.Button("Start the analysis", variant="primary")
+            
+        with gr.Column():
+            output_json = gr.JSON(label="Analysis result")
+            
+    # 示例数据
+    gr.Examples(
+        examples=[[
+            """John Smith
+            Senior Python Developer
+            Skills: Python, Django, AWS, Machine Learning
+            Experience: 5+ years at Google, 3 years at Amazon
+            Education: MIT Computer Science"""
+        ]],
+        inputs=[input_text]
+    )
+
+    submit_btn.click(
+        fn=analyze_resume,
+        inputs=[input_text],
+        outputs=output_json
+    )
+
+# 启动应用
+if __name__ == "__main__":
+    demo.launch()