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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()
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