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app.py

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+
+import streamlit as st
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration, pipeline
+from gtts import gTTS
+import os
+import tempfile
+
+@st.cache_resource
+def load_models():
+    processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
+    blip_model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
+    gpt2_pipeline = pipeline("text-generation", model="gpt2")
+    return processor, blip_model, gpt2_pipeline
+
+processor, blip_model, gpt2 = load_models()
+
+st.title("🖼️📖 Storyteller for Kids")
+st.write("Upload an image and let the app create and read a magical story just for kids!")
+
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    with st.spinner("Generating image caption..."):
+        inputs = processor(images=image, return_tensors="pt")
+        out = blip_model.generate(**inputs)
+        caption = processor.decode(out[0], skip_special_tokens=True)
+        st.success("Caption generated!")
+        st.write(f"**Caption:** {caption}")
+
+    with st.spinner("Writing a children's story..."):
+        prompt = f"Write a story for children aged 3-10 about this: {caption}"
+        story_output = gpt2(prompt, max_length=120, do_sample=True)[0]["generated_text"]
+        story = story_output.strip().replace('\n', ' ')
+        st.success("Story created!")
+        st.write(f"**Story:**\n\n{story}")
+
+    with st.spinner("Converting story to audio..."):
+        tts = gTTS(text=story, lang='en')
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+            tts.save(fp.name)
+            st.audio(fp.name, format="audio/mp3")

requirements.txt

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+streamlit
+transformers
+torch
+Pillow
+gtts