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app.py

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+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline
+import requests
+import os
+import streamlit as st
+
+load_dotenv(find_dotenv())
+api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+#img2text
+def img2text(url):
+    image_to_text = pipeline("image-to-text",model='Salesforce/blip-image-captioning-large')
+
+    text = image_to_text(url)[0]["generated_text"]
+    #print(text)
+    return text
+#
+
+#text2speech
+def text2speech(message):
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    #API_URL = "https://api-inference.huggingface.co/models/microsoft/speecht5_tts"
+    headers = {"Authorization": f"Bearer {api_token}"}
+    payloads = {
+         "inputs":message
+    }
+    response = requests.post(API_URL, headers=headers, json=payloads)
+    with open('audio.flac','wb') as file:
+        file.write(response.content)
+
+
+def main():
+    st.title("Image to text to audio by 🤖")
+    st.header("Turn image to audio podcast !!!")
+    st.caption("Sample picture...")
+    st.image("beachboat.jpg")
+    img2text("beachboat.jpg")
+    uploaded_file = st.file_uploader("Choose your image or simpley drag sample image given above",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)
+        scenario = img2text(uploaded_file.name)
+        text2speech(scenario)
+
+        with st.expander("Scenario"):
+            st.write(scenario)
+        
+        st.audio("audio.flac")
+
+if __name__ == '__main__':
+    main()
+        

requirements.txt

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+streamlit
+os
+requests
+transformers
+dotenv