Update app.py

app.py

@@ -1,30 +1,53 @@
+# import part
+!pip install streamlit pyngrok
 import streamlit as st
 from transformers import pipeline
+from PIL import Image
+import io
 
-# Load the text classification model pipeline
-classifier = pipeline("text-classification",model='isom5240ust/bert-base-uncased-emotion', return_all_scores=True)
+  
+# function part
+def generate_image_caption(image):
+    """Generates a caption for the given image using a pre-trained model."""
+    img2caption = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    result = img2caption(image)
+    return result[0]['generated_text']
 
-# Streamlit application title
-st.title("Text Classification for you")
-st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+def text2story(text):
+    """Generates a children's story from text input with genre adaptation"""
+    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"
+    
+    pipe = pipeline("text-generation", model="pranavpsv/genre-story-generator-v2")
+    story_text = pipe(
+        story_prompt,
+        max_new_tokens=200,
+        temperature=0.9,
+        top_k=50
+    )[0]['generated_text']
+    return story_text.split("Happy ending")[-1].strip()  # Clean output
 
-# Text input for user to enter the text to classify
-text = st.text_area("Enter the text to classify", "")
+def main():
+    st.title("📖 Image Story Generator")
+    st.write("Upload an image and get a magical children's story!")
 
-# Perform text classification when the user clicks the "Classify" button
-if st.button("Classify"):
-    # Perform text classification on the input text
-    results = classifier(text)[0]
+    uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-    # Display the classification result
-    max_score = float('-inf')
-    max_label = ''
+    if uploaded_image:
+        image = Image.open(uploaded_image).convert("RGB")
+        st.image(image, use_column_width=True)
+        
+        with st.spinner("✨ Analyzing image..."):
+            caption = generate_image_caption(image)
+        
+        st.subheader("Image Understanding")
+        st.write(caption)
+        
+        with st.spinner("📖 Writing story..."):
+            story = text2story(caption)
+        
+        st.subheader("Magical Story")
+        st.write(story)
 
-    for result in results:
-        if result['score'] > max_score:
-            max_score = result['score']
-            max_label = result['label']
-
-    st.write("Text:", text)
-    st.write("Label:", max_label)
-    st.write("Score:", max_score)
+if __name__ == "__main__":
+    main()