Delete 321Backup_app.py

321Backup_app.py

@@ -1,70 +0,0 @@
-import streamlit as st
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-import requests
-from PIL import Image
-import io
-
-def load_image(input_type, uploaded_file=None, image_url=""):
-    """
-    Loads an image from an uploaded file or URL.
-    """
-    if input_type == "Upload Image" and uploaded_file is not None:
-        return Image.open(io.BytesIO(uploaded_file.getvalue()))
-    elif input_type == "Image URL" and image_url:
-        try:
-            response = requests.get(image_url)
-            return Image.open(io.BytesIO(response.content))
-        except Exception as e:
-            st.error(f"Error loading image from URL: {e}")
-            return None
-
-def image_to_caption(image, input_type, uploaded_file, image_url):
-    """
-    Generates a caption for the given image.
-    """
-    image_to_text_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-    if input_type == "Upload Image" and uploaded_file:
-        return image_to_text_pipeline(uploaded_file.getvalue())[0]['generated_text']
-    elif input_type == "Image URL" and image_url:
-        return image_to_text_pipeline(image_url)[0]['generated_text']
-    return ""
-
-def generate_text_from_caption(caption):
-    """
-    Generates text based on the provided caption.
-    """
-    model = AutoModelForCausalLM.from_pretrained('pranavpsv/genre-story-generator-v2')
-    tokenizer = AutoTokenizer.from_pretrained('pranavpsv/genre-story-generator-v2')
-    input_ids = tokenizer.encode(caption, return_tensors='pt')
-    generated_outputs = model.generate(input_ids, max_length=100, num_return_sequences=1)
-    return tokenizer.decode(generated_outputs[0], skip_special_tokens=True)
-
-def main():
-    st.title('Image to Story Converter')
-
-    # User interface for input selection
-    input_type = st.radio("Select input type:", ("Upload Image", "Image URL"))
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) if input_type == "Upload Image" else None
-    image_url = st.text_input("Enter the image URL here:", "") if input_type == "Image URL" else ""
-
-    # Load image based on input selection
-    image = load_image(input_type, uploaded_file, image_url)
-    if image:
-        st.image(image, caption='Selected Image', use_column_width=True)
-
-    # Process image and generate text
-    if st.button('Generate Caption and Continue'):
-        if image:
-            with st.spinner("Processing..."):
-                # Convert image to text
-                caption = image_to_caption(image, input_type, uploaded_file, image_url)
-                st.success(f'Caption: {caption}')
-
-                # Generate additional text based on the caption
-                generated_text = generate_text_from_caption(caption)
-                st.text_area("Generated Story:", generated_text, height=200)
-        else:
-            st.error("Please upload an image or enter an image URL first.")
-
-if __name__ == "__main__":
-    main()