Update app.py

app.py

@@ -11,26 +11,46 @@ def img2text(url):
 
 # text2story
 def text2story(text):
-    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
-    # 构造 prompt，但只用于内部生成，不让模型在最终输出中重复 prompt
-    prompt = (
-        f"Write a complete fairy tale story for children aged 3-10. "
-        f"The story should be engaging, imaginative, and written in clear simple language. "
-        f"Make sure the story is between 50 and 150 words. "
-        f"Use the following scene description as inspiration: {text}"
-    )
-    # 通过 return_full_text=False，避免将 prompt 原样拼接在输出里
-    # 同时使用 max_new_tokens 控制生成长度，防止生成被截断或过长
-    story_text = pipe(
-        prompt,
-        max_new_tokens=250,       # 根据需要可适当调大，以保证字数
-        min_new_tokens=50,        # 保证至少生成一定长度
+    # 结构化prompt模板（强制模型遵循起承转合）
+    prompt = f"""Write a COMPLETE fairy tale for children aged 3-10. Follow these rules:
+[STORY STRUCTURE]
+1. BEGINNING: Introduce characters and setting (20-30 words)
+2. MIDDLE:  Describe a magical problem (40-60 words)
+3. ENDING:   Show how kindness/courage solves it (30-40 words)
+
+[EXAMPLE]
+Input: "a forest with glowing mushrooms"
+Output: "Little fox Luna found glowing mushrooms deep in the forest. They were fairy homes! When a storm threatened to destroy them, Luna bravely built a leaf umbrella to protect the tiny homes. The grateful fairies made her forest guardian, and they danced under moonbeams every night."
+
+[INPUT SCENE]: {text}
+[STORY]:"""  # 用方括号明确区分指令和内容
+
+    pipe = pipeline(
+        "text-generation", 
+        model="pranavpsv/genre-story-generator-v2",
+        max_new_tokens=300,            # 预留冗余空间
+        min_new_tokens=100,
         do_sample=True,
-        top_p=0.95,
-        temperature=0.8,
-        return_full_text=False
-    )[0]['generated_text']
-    return story_text
+        temperature=0.65,             # 降低随机性
+        top_p=0.85,
+        top_k=30,                     # 加速生成
+        repetition_penalty=1.3,
+        no_repeat_ngram_size=3,       # 防止重复短语
+        early_stopping=False          # 避免提前终止
+    )
+    
+    # 生成并后处理
+    raw_output = pipe(prompt, return_full_text=False)[0]['generated_text']
+    
+    # 提取核心内容（适配模型输出模式）
+    if "[STORY]:" in raw_output:
+        story = raw_output.split("[STORY]:")[-1].strip()
+    else:
+        story = raw_output
+    
+    # 智能截断机制
+    target_length = min(max(len(story.split()), 150)  # 动态适配
+    return " ".join(story.split()[:target_length])
 
 
 # text2audio