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+# Use a lightweight Python 3.9 image as the base
+FROM python:3.9.20-slim-bullseye
+
+# Set the working directory within the container where your application code resides
+WORKDIR /app
+
+# Copy the requirements.txt file that specifies application's dependencies
+COPY requirements.txt ./
+
+# Install the dependencies listed in requirements.txt using pip3
+RUN pip3 install --upgrade pip && pip3 install -r requirements.txt
+
+# Copy all files from the current directory (.) on the host machine
+# to the /app directory within the container
+COPY . .
+
+# Expose port 8501 to make Streamlit application accessible from outside the container
+EXPOSE 8501
+
+# Define the command to execute when the container starts. This will run Streamlit 
+# and execute your application code located in app.py
+CMD ["streamlit", "run", "app.py"]

app.py

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+import os
+import streamlit as st
+import requests
+from transformers import pipeline
+from typing import Dict
+from together import Together
+
+# Image-to-text
+def img2txt(url: str) -> str:
+    print("Initializing captioning model...")
+    captioning_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    
+    print("Generating text from the image...")
+    text = captioning_model(url, max_new_tokens=20)[0]["generated_text"]
+    
+    print(text)
+    return text
+
+# Text-to-story generation with LLM model
+def txt2story(prompt: str, top_k: int, top_p: float, temperature: float) -> str:
+    # Load the Together API client
+    client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
+
+    # Modify the prompt based on user inputs and ensure a 250-word limit
+    story_prompt = f"Write a short story of no more than 250 words based on the following prompt: {prompt}"
+
+    # Call the LLM model
+    stream = client.chat.completions.create(
+        model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
+        messages=[
+            {"role": "system", "content": '''As an experienced short story writer, write a meaningful story influenced by the provided prompt.
+            Ensure the story does not exceed 250 words.'''},
+            {"role": "user", "content": story_prompt}
+        ],
+        top_k=top_k,
+        top_p=top_p,
+        temperature=temperature,
+        stream=True
+    )
+
+    # Concatenate story chunks
+    story = ''
+    for chunk in stream:
+        story += chunk.choices[0].delta.content
+
+    return story
+
+# Text-to-speech
+def txt2speech(text: str) -> None:
+    print("Initializing text-to-speech conversion...")
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACEHUB_API_TOKEN']}"}
+    payloads = {'inputs': text}
+    
+    response = requests.post(API_URL, headers=headers, json=payloads)
+    
+    with open('audio_story.mp3', 'wb') as file:
+        file.write(response.content)
+
+# Get user preferences for the story
+def get_user_preferences() -> Dict[str, str]:
+    preferences = {}
+
+    preferences['continent'] = st.selectbox("Continent", ["North America", "Europe", "Asia", "Africa", "Australia"])
+    preferences['genre'] = st.selectbox("Genre", ["Science Fiction", "Fantasy", "Mystery", "Romance"])
+    preferences['setting'] = st.selectbox("Setting", ["Future", "Medieval times", "Modern day", "Alternate reality"])
+    preferences['plot'] = st.selectbox("Plot", ["Hero's journey", "Solving a mystery", "Love story", "Survival"])
+    preferences['tone'] = st.selectbox("Tone", ["Serious", "Light-hearted", "Humorous", "Dark"])
+    preferences['theme'] = st.selectbox("Theme", ["Self-discovery", "Redemption", "Love", "Justice"])
+    preferences['conflict'] = st.selectbox("Conflict Type", ["Person vs. Society", "Internal struggle", "Person vs. Nature", "Person vs. Person"])
+    preferences['twist'] = st.selectbox("Mystery/Twist", ["Plot twist", "Hidden identity", "Unexpected ally/enemy", "Time paradox"])
+    preferences['ending'] = st.selectbox("Ending", ["Happy", "Bittersweet", "Open-ended", "Tragic"])
+    
+    return preferences
+
+# Main function
+def main():
+    st.set_page_config(page_title="🎨 Image-to-Audio Story 🎧", page_icon="🖼️")
+    st.title("Turn the Image into Audio Story")
+
+    # Allows users to upload an image file
+    uploaded_file = st.file_uploader("# 📷 Upload an image...", type=["jpg", "jpeg", "png"])
+
+    # Parameters for LLM model (in the sidebar)
+    st.sidebar.markdown("# LLM Inference Configuration Parameters")
+    top_k = st.sidebar.number_input("Top-K", min_value=1, max_value=100, value=5)
+    top_p = st.sidebar.number_input("Top-P", min_value=0.0, max_value=1.0, value=0.8)
+    temperature = st.sidebar.number_input("Temperature", min_value=0.1, max_value=2.0, value=1.5)
+
+    # Get user preferences for the story
+    st.markdown("## Story Preferences")
+    preferences = get_user_preferences()
+
+    if uploaded_file is not None:
+        # Reads and saves uploaded image file
+        bytes_data = uploaded_file.read()
+        with open("uploaded_image.jpg", "wb") as file:
+            file.write(bytes_data)
+
+        st.image(uploaded_file, caption='🖼️ Uploaded Image', use_column_width=True)
+
+        # Initiates AI processing and story generation
+        with st.spinner("## 🤖 AI is at Work! "):
+            scenario = img2txt("uploaded_image.jpg")  # Extracts text from the image
+            
+            # Modify the prompt to include user preferences
+            prompt = f"Based on the image description: '{scenario}', create a {preferences['genre']} story set in {preferences['setting']} in {preferences['continent']}. " \
+                     f"The story should have a {preferences['tone']} tone and explore the theme of {preferences['theme']}. " \
+                     f"The main conflict should be {preferences['conflict']}. " \
+                     f"The story should have a {preferences['twist']} and end with a {preferences['ending']} ending."
+            
+            story = txt2story(prompt, top_k, top_p, temperature)  # Generates a story based on the image text, LLM params, and user preferences
+            
+            txt2speech(story)  # Converts the story to audio
+
+            st.markdown("---")
+            st.markdown("## 📜 Image Caption")
+            st.write(scenario)
+
+            st.markdown("---")
+            st.markdown("## 📖 Story")
+            st.write(story)
+
+            st.markdown("---")
+            st.markdown("## 🎧 Audio Story")
+            st.audio("audio_story.mp3")
+
+if __name__ == '__main__':
+    main()
+
+# Credits
+st.markdown("### Credits")
+st.caption('''
+            Made with ❤️ by @Aditya-Neural-Net-Ninja\n 
+            Utilizes Image-to-Text, Text Generation, Text-to-Speech Transformer Models\n
+            Gratitude to Streamlit, 🤗 Spaces for Deployment & Hosting
+            ''')

requirements.txt

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+tf-keras==2.17.0
+tensorflow==2.17.0
+transformers==4.45.1
+huggingface_hub==0.25.1
+pillow==10.4.0
+streamlit==1.38.0
+together==1.3.0