Create app.py

app.py

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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_path):
+    try:
+        # Use the OCR model to extract a caption/description from the image
+        result = ocr_model(Image.open(image_path))
+        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, None
+
+        # 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()