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| import gradio as gr | |
| from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer | |
| from PIL import Image | |
| # Load the Image-to-Text (OCR) model | |
| ocr_model = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning") | |
| # Load the Text Generation model | |
| story_model_name = "EleutherAI/gpt-neo-2.7B" | |
| story_tokenizer = AutoTokenizer.from_pretrained(story_model_name) | |
| story_model = AutoModelForCausalLM.from_pretrained(story_model_name) | |
| # Function to extract text description from an image | |
| def extract_description(image_array): | |
| try: | |
| # Convert the NumPy array to a PIL image | |
| image = Image.fromarray(image_array) | |
| # Use the OCR model to extract a caption/description from the image | |
| result = ocr_model(image) | |
| return result[0]["generated_text"] | |
| except Exception as e: | |
| return f"Error extracting description: {e}" | |
| # Function to generate a story based on the extracted description | |
| def generate_story(description): | |
| try: | |
| # Format the input prompt for the story | |
| prompt = f"Create a creative story based on this description: {description}" | |
| # Use the story model to generate text | |
| inputs = story_tokenizer(prompt, return_tensors="pt", truncation=True) | |
| outputs = story_model.generate(inputs["input_ids"], max_length=300, num_return_sequences=1, temperature=0.8) | |
| story = story_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return story | |
| except Exception as e: | |
| return f"Error generating story: {e}" | |
| # Main function to process the image and generate a story | |
| def create_story(image): | |
| try: | |
| # Step 1: Extract a description from the image | |
| description = extract_description(image) | |
| if not description or "Error" in description: | |
| return description | |
| # Step 2: Generate a story from the extracted description | |
| story = generate_story(description) | |
| # Combine the description and story for the output | |
| output = f"📷 Extracted Description:\n{description}\n\n📖 Generated Story:\n{story}" | |
| return output | |
| except Exception as e: | |
| return f"Error processing the image: {e}" | |
| # Gradio interface | |
| interface = gr.Interface( | |
| fn=create_story, | |
| inputs=gr.Image(label="Upload an Image (PNG, JPG, JPEG)"), | |
| outputs=gr.Textbox(label="Generated Story"), | |
| title="Text-Based Story Creator", | |
| description=( | |
| "Upload an image, and this app will generate a creative story based on the description of the image. " | |
| "It uses advanced AI models for image-to-text conversion and story generation." | |
| ), | |
| allow_flagging="never" | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() | |