Update app.py

app.py

@@ -15,17 +15,17 @@ if not groq_key:
 
 client = Groq(api_key=groq_key)
 
-class SpellChecker:
+class EssayEvaluator:
     def __init__(self):
         pass
 
     def check_spelling(self, text):
         completion = client.chat.completions.create(
-            model="llama-3.1-70b-versatile",
+            model="llama3-8b-8192",
             messages=[
                 {
                     "role": "system",
-                    "content": "你是一個中文老師，專門挑出錯字，會將錯字列表，並用 ZH-TW 繁體中文指導我，example: 輸入：你是一個人累 ，輸出：你是一個人「累」，錯誤訂正：類"
+                    "content": "你是一個老師，請幫我挑錯字。"
                 },
                 {
                     "role": "user",
@@ -38,24 +38,78 @@ class SpellChecker:
             stream=False,
             stop=None,
         )
+
+        response_content = ""
+        for choice in completion.choices:
+            if hasattr(choice, 'message'):
+                response_content += choice.message.content
         
-        # Assuming the response contains a 'choices' list with 'message' objects
-        response_content = ''.join([chunk.message.content for chunk in completion.choices])
-        
         return response_content
 
-spell_checker = SpellChecker()
+    def overall_evaluation(self, text):
+        completion = client.chat.completions.create(
+            model="llama3-8b-8192",
+            messages=[
+                {
+                    "role": "system",
+                    "content": "你是一個老師，請幫我評價這篇作文。"
+                },
+                {
+                    "role": "user",
+                    "content": text
+                }
+            ],
+            temperature=1,
+            max_tokens=1024,
+            top_p=1,
+            stream=False,
+            stop=None,
+        )
+
+        response_content = ""
+        for choice in completion.choices:
+            if hasattr(choice, 'message'):
+                response_content += choice.message.content
+
+        return response_content
+
+evaluator = EssayEvaluator()
 
-def check_spelling_interface(text):
-    return spell_checker.check_spelling(text)
+def correct_essay(text):
+    feedback = evaluator.check_spelling(text)
+    evaluation = evaluator.overall_evaluation(text)
 
-with gr.Blocks(title="中文錯字檢查") as demo:
-    gr.Markdown("# 中文錯字檢查")
-    
-    input_text = gr.Textbox(placeholder="輸入全文...", label="你的文本", lines=10)
-    check_button = gr.Button("檢查錯字")
-    output_text = gr.Textbox(label="錯字列表", lines=10)
+    word_count = len(text.split())
+    sentence_count = len(text.split('. '))
+    avg_sentence_length = word_count / sentence_count if sentence_count else 0
+
+    feedback_html = '<h3>作文評價：</h3>'
+    feedback_html += f'<p>字數：{word_count}字</p>'
+    feedback_html += f'<p>句子數：{sentence_count}句</p>'
+    feedback_html += f'<p>平均句子長度：{avg_sentence_length:.2f}字/句</p>'
+
+    if word_count < 200:
+        feedback_html += '<p>建議：作文篇幅稍短，可以考慮增加一些細節描述或論述來豐富內容。</p>'
+    elif word_count > 800:
+        feedback_html += '<p>建議：作文篇幅較長，要注意是否有冗余的內容，可以考慮精簡一些不必要的描述。</p>'
+
+    if avg_sentence_length > 20:
+        feedback_html += '<p>建議：句子平均長度偏長，可以考慮將一些較長的句子拆分，以提高可讀性。</p>'
+    elif avg_sentence_length < 8:
+        feedback_html += '<p>建議：句子平均長度偏短，可以考慮將一些短句合併，使文章更加流暢。</p>'
+
+    feedback_html += f'<p>AI反饋：{feedback}</p>'
+    feedback_html += f'<p>整體評價：{evaluation}</p>'
+
+    return feedback_html
+
+with gr.Blocks(title="AI作文批改助手") as demo:
+    gr.Markdown("# AI作文批改助手")
     
-    check_button.click(check_spelling_interface, inputs=input_text, outputs=output_text)
+    essay_input = gr.Textbox(placeholder="請在這裡粘貼您的作文...", label="你的文本", lines=10)
+    correct_button = gr.Button("批改作文")
+    feedback_output = gr.HTML(label="批改反饋")
+
+    correct_button.click(correct_essay, inputs=essay_input, outputs=feedback_output)
     
     demo.launch(share=True)