Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# img2text
+def img2text(url):
+    image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
+    text = image_to_text_model(url)[0]["generated_text"]
+
+    print(text)
+    return text
+
+# txt2Story
+def txt2story(text):
+    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
+    story_txt = pipe(text)[0]['generated_text']
+
+    print(story_txt)
+    return story_txt
+
+# Story2Audio
+def text2audio(story_text):
+    pipe = pipeline("text-to-audio", model="Matthijs/mms-tts-eng")
+    audio_data = pipe(story_text)
+    return audio_data
+
+
+def main():
+    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...")
+
+    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 = txt2story(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'])
+
+
+if __name__ == "__main__":
+    main()