Create app.py

app.py

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+# Imports
+import os
+import streamlit as st
+import requests
+from transformers import pipeline
+import openai
+
+# Suppressing all warnings
+import warnings
+warnings.filterwarnings("ignore")
+
+# Image-to-text
+def img2txt(url):
+    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 generated successfully.")
+    return text
+
+# Text-to-story
+def txt2story(img_text):
+    print("Initializing client...")
+    client = openai.OpenAI(
+        api_key=os.environ["TOGETHER_API_KEY"], 
+        base_url='https://api.together.xyz',
+    )
+
+    print("Constructing prompt for story generation...")
+    content_prompt = f'''Based on the image description "{img_text}", conclude the story. 
+    Resolve the conflict or summarize the outcome of the situation. Ensure the story MUST
+    have a definitive ending. The end.'''
+
+    print("Preparing message sequences for interaction...")
+    messages = [
+        {"role": "system", "content": "Once upon a time..."},
+        {"role": "user", "content": img_text, "temperature": 1},
+        {"role": "system", 
+         "content": content_prompt, 
+         "temperature": 0.7},
+    ]
+    
+    print("Generating story completion using the AI model...")
+    chat_completion = client.chat.completions.create(
+        messages=messages,
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        max_tokens=200)
+
+    print("Story generated successfully.")
+    return chat_completion.choices[0].message.content
+
+
+# Text-to-speech
+def txt2speech(text):
+    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}
+
+    print("Sending request for speech synthesis...")
+    response = requests.post(API_URL, headers=headers, json=payloads)
+    
+    print("Saving synthesized speech to audio file...")
+    with open('audio_story.mp3', 'wb') as file:
+        file.write(response.content)
+    
+    print("Text-to-speech conversion completed.")
+
+
+# Streamlit web app 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"])
+
+    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
+            story = txt2story(scenario)  # Generates a story based on the image text
+            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
+            ''')