Update app.py

app.py

@@ -3,50 +3,114 @@ import streamlit as st
 from transformers import pipeline
 from PIL import Image
 import io
+import numpy as np
+import soundfile as sf  # For handling audio file operations
 
-  
 # function part
 def generate_image_caption(image):
-    """Generates a caption for the given image using a pre-trained model."""
+    """Generates a caption for the given image using a pre-trained model.
+    Args:
+        image: PIL Image object
+    Returns:
+        str: Generated caption text
+    """
+    # Initialize image-to-text pipeline with BLIP model
     img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
     result = img2caption(image)
     return result[0]['generated_text']
 
 def text2story(text):
-    """Generates a children's story from text input with genre adaptation"""
+    """Generates a children's story from text input using story generation model.
+    Args:
+        text: Input text prompt
+    Returns:
+        str: Generated story text
+    """
+    # Craft prompt with specific requirements for children's stories
     story_prompt = f"Create a funny 100-word story for 8-year-olds about: {text}. Include: "
     story_prompt += "1) A silly character 2) Magical object 3) Sound effects 4) Happy ending"
     
+    # Initialize text generation pipeline
     pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
+    
+    # Generate story with controlled randomness parameters
     story_text = pipe(
         story_prompt,
-        max_new_tokens=200,
-        temperature=0.9,
-        top_k=50
+        max_new_tokens=200,  # Limit story length
+        temperature=0.9,     # Control randomness (higher = more creative)
+        top_k=50             # Limit vocabulary choices
     )[0]['generated_text']
-    return story_text.split("Happy ending")[-1].strip()  # Clean output
+    
+    # Clean output by splitting at the required ending marker
+    return story_text.split("Happy ending")[-1].strip()
+
+def story_to_speech(story_text):
+    """Converts story text to audio using text-to-speech model.
+    Args:
+        story_text: Story text to convert
+    Returns:
+        BytesIO: Audio data in WAV format
+    """
+    # Initialize Bark text-to-speech pipeline
+    tts_pipe = pipeline("text-to-speech", model="suno/bark-small")
+    
+    # Generate audio array (numpy array of sound samples)
+    audio_output = tts_pipe(story_text, max_length=400)  # Limit text length for stability
+    
+    # Convert numpy array to playable audio bytes
+    audio_bytes = io.BytesIO()
+    sf.write(
+        audio_bytes, 
+        audio_output["audio"], 
+        audio_output["sampling_rate"], 
+        format='WAV'
+    )
+    audio_bytes.seek(0)  # Reset pointer for Streamlit audio player
+    
+    return audio_bytes
 
 def main():
-    st.title("📖 Image Story Generator")
-    st.write("Upload an image and get a magical children's story!")
+    """Main function for Streamlit application workflow"""
+    # Configure page header
+    st.title("📖 Image Story Generator with Audio")
+    st.write("Upload an image to get a magical story read aloud!")
 
+    # Image upload widget (supports JPG/PNG)
     uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
     if uploaded_image:
-        image = Image.open(uploaded_image).convert("RGB")
-        st.image(image, use_column_width=True)
+        # Process image
+        image = Image.open(uploaded_image).convert("RGB")  # Ensure RGB format
+        st.image(image, use_column_width=True)  # Display uploaded image
         
+        # Image analysis section
         with st.spinner("✨ Analyzing image..."):
             caption = generate_image_caption(image)
         
+        # Display image understanding
         st.subheader("Image Understanding")
         st.write(caption)
         
+        # Story generation section
         with st.spinner("📖 Writing story..."):
             story = text2story(caption)
         
+        # Display generated story
         st.subheader("Magical Story")
         st.write(story)
+        
+        # Audio generation section
+        if st.button("🎧 Read Story Aloud"):
+            with st.spinner("🔊 Generating audio..."):
+                try:
+                    # Convert story to audio (trim to 400 characters for model stability)
+                    audio_bytes = story_to_speech(story[:400])
+                    
+                    # Display audio player
+                    st.audio(audio_bytes, format="audio/wav")
+                except Exception as e:
+                    st.error(f"Error generating audio: {str(e)}")
 
 if __name__ == "__main__":
+    # Start the Streamlit application
     main()