Update app.py

app.py

@@ -8,7 +8,6 @@ def load_pipeline1_model():
     model_name = "yitongwu73/finetuned-roberta-base"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForSequenceClassification.from_pretrained(model_name)
-    # 回归任务需手动设置pipeline类型为"text-classification"并指定回归参数
     pipe = pipeline(
         "text-classification",
         model=model,
@@ -32,48 +31,49 @@ def load_pipeline2_model():
 
 def main():
     st.set_page_config(
-        page_title="IELTS作文自动评分与反馈系统",
+        page_title="IELTS Essay Scoring & Feedback System",
         page_icon="📝",
         layout="wide"
     )
     
-    st.title("NewChannel IELTS作文智能评估系统")
-    st.markdown("### 请输入作文题目和正文")
+    st.title("NewChannel IELTS Essay Evaluation System")
+    st.markdown("### Please enter your essay prompt and content")
     
     # 输入框布局
     with st.form("evaluation_form"):
-        prompt = st.text_area("作文题目（Prompt）", height=100)
-        essay = st.text_area("作文正文（Essay）", height=300)
-        submitted = st.form_submit_button("生成评估报告")
+        prompt = st.text_area("Essay Prompt", height=100)
+        essay = st.text_area("Essay Content", height=300)
+        submitted = st.form_submit_button("Generate Evaluation Report")
         
         if submitted:
             if not prompt or not essay:
-                st.warning("请填写完整作文题目和正文")
+                st.warning("Please complete both the essay prompt and content")
                 return
             
             # 显示加载状态
-            with st.spinner("正在评估作文..."):
+            with st.spinner("Evaluating your essay..."):
                 # Pipeline 1: 获取整体评分
                 pipe1 = load_pipeline1_model()
-                # 注意：回归模型输出为logits，需手动转换为连续分数（示例假设输出为0-9的连续值）
-                # 实际需根据模型输出调整，例如是否使用sigmoid或直接返回logits
-                input_text = f"评分任务：题目：{prompt} 作文：{essay}"
+                input_text = f"Scoring Task: Prompt: {prompt} Essay: {essay}"
                 score_output = pipe1(input_text)[0]
-                # 示例处理：假设模型输出包含"label"为分数（需根据实际模型输出调整）
-                # 若输出为logits，可能需要归一化到0-9区间
-                overall_score = score_output.get('score', 0.0) * 9.0  # 示例转换，需根据模型实际输出调整
-                overall_score = round(overall_score, 1)  # 保留一位小数
+                
+                # 分数映射和转换（保持与之前相同的逻辑）
+                logits = score_output['score']
+                min_logit, max_logit = -5, 5  # 需根据模型实际输出范围调整
+                min_score, max_score = 3.5, 9.0
+                overall_score = ((logits - min_logit) / (max_logit - min_logit)) * (max_score - min_score) + min_score
+                overall_score = round(overall_score * 2) / 2  # 保留0.5间隔
                 
                 # Pipeline 2: 生成详细反馈
                 pipe2 = load_pipeline2_model()
-                input_text2 = f"生成反馈：题目：{prompt} 作文：{essay}"
+                input_text2 = f"Generate Feedback: Prompt: {prompt} Essay: {essay}"
                 feedback_output = pipe2(input_text2, max_length=1024, num_return_sequences=1)[0]
                 feedback_text = feedback_output['generated_text']
                 
-            # 展示结果
-            st.markdown("### 评估结果")
-            st.markdown(f"#### 整体分数：**{overall_score}/9.0**")
-            st.markdown("#### 详细反馈")
+            # 展示结果（英文）
+            st.markdown("### Evaluation Results")
+            st.markdown(f"#### Overall Band Score: **{overall_score}/9.0**")
+            st.markdown("#### Detailed Feedback")
             st.markdown(feedback_text)
 
 if __name__ == "__main__":