Update app.py

app.py

@@ -1,50 +1,58 @@
-import streamlit as st
+# import part
 from transformers import pipeline
-from PIL import Image
-import soundfile as sf
-import io
-
-# 1. 加载Pipeline
-#   - 图像→文本：使用 nlpconnect/vit-gpt2-image-captioning
-#   - 文本→语音：使用 facebook/mms-tts 或其它 TTS 模型
-img_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-text_to_speech = pipeline("text-to-speech", model="facebook/mms-tts")
-
-st.title("Image-to-Text and Text-to-Speech App (WAV output)")
-
-# 2. 上传图片
-uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
-if uploaded_image:
-    # 显示图片
-    img = Image.open(uploaded_image)
-    st.image(img, caption="Uploaded Image", use_container_width=True)
-
-    # 3. 图像 → 文本
-    text_output = img_to_text(img)[0]["generated_text"]
-    st.write("### Extracted Text")
-    st.write(text_output)
-
-    # 4. 文本 → 语音 (TTS)
-    #    text_to_speech(...) 返回一个 dict，包含 "audio" (numpy数组) 和 "sampling_rate"
-    st.write("### Listen to Speech Output")
-    speech_output = text_to_speech(text_output)
-
-    # 5. 将返回的音频数组写到内存中的 WAV 文件
-    audio_array = speech_output["audio"]           # numpy array
-    sample_rate = speech_output["sampling_rate"]   # 采样率
-
-    wav_io = io.BytesIO()
-    # 利用 soundfile 将音频数组写入内存，并指定格式为 WAV
-    sf.write(wav_io, audio_array, sample_rate, format="WAV")
-    wav_io.seek(0)  # 将指针重置到开头，方便后续读取
-
-    # 6. 使用 st.audio 播放内存中的 WAV
-    st.audio(wav_io, format="audio/wav")
-
-    # 7. (可选) 提供下载按钮，下载 WAV 文件
-    st.download_button(
-        label="Download WAV",
-        data=wav_io,
-        file_name="speech.wav",
-        mime="audio/wav"
-    )
+import streamlit as st
+
+# function part
+# img2text
+def img2text(url):
+    image_to_text_model = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+    text = image_to_text_model(url)[0]["generated_text"]
+    return text
+
+# text2story
+def text2story(text):
+    story_text = pipeline("text-generation", model="distilbert/distilgpt2")
+    return story_text
+
+# text2audio
+def text2audio(story_text):
+    audio_data = pipeline("text-to-audio", model="facebook/musicgen-medium")
+    return audio_data
+
+# main part
+st.set_page_config(page_title="Your Image to Audio Story",
+                   page_icon="🦜")
+st.header("Turn Your Image to Audio Story")
+
+# Upload image here
+uploaded_file = st.file_uploader("Select an Image...")
+
+if uploaded_file is not None:
+    print(uploaded_file)
+    bytes_data = uploaded_file.getvalue()
+    with open(uploaded_file.name, "wb") as file:
+        file.write(bytes_data)
+    st.image(uploaded_file, caption="Uploaded Image",
+             use_column_width=True)
+
+    #Stage 1: Image to Text
+    st.text('Processing img2text...')
+    scenario = img2text(uploaded_file.name)
+    st.write(scenario)
+
+    #Stage 2: Text to Story
+    st.text('Generating a story...')
+    story = text2story(scenario)
+    st.write(story)
+
+    #Stage 3: Story to Audio data
+    st.text('Generating audio data...')
+    audio_data =text2audio(story)
+
+    # Play button
+    if st.button("Play Audio"):
+        st.audio(audio_data['audio'],
+                    format="audio/wav",
+                    start_time=0,
+                    sample_rate = audio_data['sampling_rate'])
+        st.audio("kids_playing_audio.wav")