Create app.py

app.py

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+from transformers import pipeline
+import streamlit as st
+import os
+
+
+
+# img2text
+def img_to_text(url):
+    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
+
+    text = image_to_text(url)[0]["generated_text"]
+    return text
+
+
+# llm
+def generate_story(text):
+    generator = pipeline("text-generation", model="distilgpt2")
+
+    result = generator(text, max_length=20, num_return_sequences=1)
+    return result[0]['generated_text']
+
+
+#
+# text-to-speech
+def text_to_speech(text):
+    import requests
+
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {os.environ.get('HUGGINGFACE_API_TOKEN')}"}
+    payload = {
+        "inputs": text
+    }
+
+    response = requests.post(API_URL, headers=headers, json=payload)
+    response.raise_for_status()
+    with open('audio.flac', 'wb') as file:
+        file.write(response.content)
+
+
+
+def main():
+    st.set_page_config(page_title="img to audio story")
+    st.header("turn image to audio story")
+    uploaded_file = st.file_uploader("Choose an image ... ", type="jpg")
+
+    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)
+        text = img_to_text(uploaded_file.name)
+        story = generate_story(text)
+        text_to_speech(story)
+
+        with st.expander("text"):
+            st.write(text)
+        with st.expander("story"):
+            st.write(story)
+        st.audio("audio.flac")
+