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app.py

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+from transformers import pipeline
+import requests
+import os
+
+os.environ['Hugging_face']='Hugging_face'
+HUGGINGFACEHUB_API_TOKEN = os.getenv("Hugging_face")
+
+os.environ['OPENAI_API_KEY']='openAPI'
+
+import streamlit as st
+import tempfile
+
+
+#Image to Text Generation
+def img2text(url):
+    image_to_text = pipeline('image-to-text', model="Salesforce/blip-image-captioning-base", max_new_tokens=100)
+    text = image_to_text(url)
+    
+    # print(text[0]["generated_text"])
+    return text[0]["generated_text"]
+
+## Text to Story Generation
+#####################################################
+from langchain.chains import LLMChain
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+
+def generate_story(scenario):
+    template= """
+    You are a story teller
+    You can generate a short story based on a simple narrative, the story shoule be no more than 100 words:
+
+    CONTEXT: {scenario}
+    STORY:
+    """
+    prompt = PromptTemplate(
+        input_variables=["scenario"],
+        template=template,
+    )
+
+    chain = LLMChain(llm=OpenAI(temperature=1), prompt=prompt)
+
+    story = chain.run(scenario)
+    # print(story)
+    return story
+
+## Story to Speech Generation
+##########################################
+def text2speech(message):
+
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"}
+
+    payloads = {
+        "inputs": message
+    }
+    
+    response = requests.post(API_URL, headers=headers, json=payloads)
+
+    with open('audio.mp3', 'wb') as file:
+        file.write(response.content)
+
+
+## Integration with streamlit
+def main():
+    st.header("Turn _Images_ into Audio :red[Stories]")
+
+    uploaded_file = st.file_uploader("Choose an image..", type='jpg')
+    
+    if uploaded_file is not None:
+        bytes_data = uploaded_file.getvalue()
+        with tempfile.NamedTemporaryFile(delete=False) as file:
+            file.write(bytes_data)
+            file_path = file.name
+
+        st.image(uploaded_file, caption='Uploaded Image',use_column_width=True)
+
+        scenario = img2text(file_path)
+        story = generate_story(scenario)
+        text2speech(story)
+
+        with st.expander("Scenario"):
+            st.write(scenario)
+        with st.expander("Story"):
+            st.write(story)
+
+        st.audio("audio.mp3")
+
+if __name__ == "__main__":
+    main()