Update app.py

app.py

@@ -1,30 +1,56 @@
-import streamlit as st
-from transformers import pipeline
-
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='isom5240ust/bert-base-uncased-emotion', return_all_scores=True)
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from PIL import Image
 
-# Streamlit application title
-st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
-
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
+def generate_caption(image_path):
+    image = Image.open(image_path)
+    processor = BlipProcessor.from_pretrained("facebook/blip-image-captioning-base")
+    model = BlipForConditionalGeneration.from_pretrained("facebook/blip-image-captioning-base")
+    inputs = processor(image, return_tensors="pt")
+    output = model.generate(**inputs)
+    caption = processor.decode(output[0], skip_special_tokens=True)
+    return caption
+from transformers import pipeline
 
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
+def generate_story(caption):
+    # 使用文本生成 pipeline
+    generator = pipeline("text-generation", model="gpt2")
+    prompt = f"由以下图片得到的描述: '{caption}'，请根据这个描述生成一个完整的童话故事，故事至少100个单词。"
+    result = generator(prompt, max_length=300, num_return_sequences=1)
+    story = result[0]['generated_text']
+    # 添加字数判断，必要时进行调整或循环生成直到满足条件
+    if len(story.split()) < 100:
+        # 可以进行递归调用或其他逻辑扩充文本
+        story += " " + generate_story(caption)
+    return story
+from gtts import gTTS
 
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
+def text_to_speech(text, output_file="output.mp3"):
+    tts = gTTS(text=text, lang='en')  # 注意可根据需要选择语言或使用中文
+    tts.save(output_file)
+    return output_file
+import streamlit as st
+from PIL import Image
 
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
+def main():
+    st.title("儿童故事生成应用")
+    st.write("上传一张图片，我们将根据图片生成有趣的故事，并转换成语音播放给你听！")
+    
+    uploaded_file = st.file_uploader("选择一张图片", type=["png", "jpg", "jpeg"])
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption="上传的图片", use_column_width=True)
+        
+        # 调用图像描述函数
+        caption = generate_caption(uploaded_file)
+        st.write("图片描述:", caption)
+        
+        # 生成故事
+        story = generate_story(caption)
+        st.write("生成的故事:", story)
+        
+        # 文字转语音
+        audio_file = text_to_speech(story)
+        st.audio(audio_file, format="audio/mp3")
 
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+if __name__ == "__main__":
+    main()