Create app1.py

app1.py

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+import streamlit as st
+from PIL import Image
+import tempfile
+from transformers import pipeline
+
+# --- Stage 1: Image → Caption ---
+def generate_caption(image):
+    caption_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
+    caption = caption_pipeline(image)[0]['generated_text']
+    return caption
+
+# --- Stage 2: Caption → Story ---
+def generate_story(caption):
+    story_pipeline = pipeline("text-generation", model="gpt2")
+    prompt = f"Write a fun, short story (50-100 words) for a child based on: {caption}"
+    story = story_pipeline(prompt, max_length=100, do_sample=True)[0]['generated_text']
+    return story
+
+# --- Stage 3: Story → Audio ---
+def generate_audio(story_text):
+    tts_pipeline = pipeline("text-to-speech", model="espnet/kan-bayashi_ljspeech_vits")
+    speech = tts_pipeline(story_text)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
+        f.write(speech["audio"])
+        return f.name
+
+# --- Streamlit UI ---
+def main():
+    st.title("📖 AI Storyteller for Kids (3 Stages)")
+    st.write("Upload a child-friendly image and let the app create a story and read it out loud!")
+
+    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+    if uploaded_image:
+        image = Image.open(uploaded_image)
+        st.image(image, caption="Your uploaded image", use_column_width=True)
+
+        with st.spinner("🔍 Generating caption..."):
+            caption = generate_caption(image)
+            st.success(f"🖼️ Caption: {caption}")
+
+        with st.spinner("📝 Generating story..."):
+            story = generate_story(caption)
+            st.markdown("### 📚 Generated Story:")
+            st.write(story)
+
+        with st.spinner("🔊 Generating audio..."):
+            audio_path = generate_audio(story)
+            st.audio(audio_path, format="audio/wav")
+
+if __name__ == "__main__":
+    main()