Create app.py

app.py

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+from transformers import pipeline
+import streamlit as st
+from PIL import Image
+
+# img2text
+def img2text(url):
+    image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    text = image_to_text_model(url)[0]["generated_text"]
+    return text
+    
+# text2story
+def text2story(text):
+    text_to_story_model = pipeline("text-generation", model="distilbert/distilgpt2")
+    if isinstance(text, list):
+        text="".join(text)# Ensure input is a single string
+    story_text = text_to_story_model(text, max_length=100, num_return_sequences=1)
+    return story_text[0]['generated text']
+
+# text2audio
+def text2audio(story_text):
+    text_to_audio_model = pipeline("text-to-speech",，model="facebook/mms-tts-eng")
+    audio_data = text_to_audio_model(story_text)
+    return audio_data
+    
+#main part
+st.set_page_config(page_title="Your Image to Audio Story",
+                   page_icon="🦜")
+st.header("Turn Your Image to Story")
+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'])
+