Create app.py

app.py

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+import os
+import gradio as gr
+from transformers import pipeline, set_seed
+
+# 加载第一个pipeline，用于图片描述
+image_to_text_pipe = pipeline("image-to-text",
+                              model="Salesforce/blip-image-captioning-base")
+
+# 加载第二个pipeline，用于根据文本生成故事
+text_to_story_pipe = pipeline("text-generation",
+                              model="meta-llama/Llama-2-7b-hf")
+
+# 设置随机种子以保证结果的一致性
+set_seed(42)
+
+def generate_description(image):
+    # 从图片生成描述
+    description = image_to_text_pipe(image)
+    description_text = description[0]['generated_text']
+    return description_text
+
+def generate_story(text):
+    # 根据描述生成故事
+    story = text_to_story_pipe(text, max_length=300, num_return_sequences=1)
+    story_text = story[0]['generated_text']
+    return story_text
+
+def process_image_and_generate_story(image):
+    # 串联两个处理过程：生成描述和根据描述生成故事
+    description_text = generate_description(image)
+    story_text = generate_story(description_text)
+    return story_text
+
+# 设置Gradio接口
+iface = gr.Interface(fn=process_image_and_generate_story,
+                     inputs=gr.Image(type='pil'),
+                     outputs="text",
+                     title="Image to Story Generator",
+                     description="Upload an image and the system will generate a story based on it.")
+
+# 启动界面
+iface.launch()