Create app.py

app.py

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+# import part
+import streamlit as st
+from transformers import pipeline
+import torch
+
+# function part
+# 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):
+    pipe = pipeline("text-generation", model="gpt2")
+    story_text = pipe(text, max_length=100)[0]['generated_text']
+    return story_text
+
+# text2audio
+def text2audio(story_text):
+    pipe = pipeline("text-to-speech", model="facebook/mms-tts-eng")
+    audio_data = pipe(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 Audio Story")
+uploaded_file = st.file_uploader("Select an Image...")#, type=["jpg", "jpeg", "png"])
+
+
+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")