Upload 2 files

app.py

@@ -0,0 +1,110 @@
+
+from PIL import Image
+#importing models
+from transformers import BlipProcessor, BlipForConditionalGeneration
+from langchain_google_genai import ChatGoogleGenerativeAI
+from dotenv import load_dotenv
+import os
+from langchain.output_parsers import StructuredOutputParser, ResponseSchema
+import gradio as gr
+from diffusers import DiffusionPipeline,StableDiffusion3Pipeline
+from huggingface_hub import login
+
+load_dotenv()
+
+Hugging_face_token=os.getenv('huggingface_token')
+
+login(Hugging_face_token)
+
+
+# loading image captionning model
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large")
+
+# Set the model name for our LLMs.
+GEMINI_MODEL = "gemini-2.0-flash"
+
+# Store the API key in a variable.
+GEMINI_API_KEY = os.getenv("google_api_key")
+
+class stable_dif:
+  def __init__(self,sizes):
+    self.sizes=sizes
+
+  def model(self):
+    if self.sizes == 'medium':
+      pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-medium")
+    elif self.sizes == 'large':
+      pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3.5-large-turbo")
+    elif self.sizes == 'small':
+      pipe = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
+    return pipe
+  
+stable=stable_dif('small')
+pipe=stable.model()
+
+
+
+def image_story_generator(image,requirement,style):
+
+  raw_image = Image.open(image)
+
+  # get caption from image
+  inputs = processor(raw_image, return_tensors="pt")
+  out = model.generate(**inputs, min_length=20)
+  model_prompt=processor.decode(out[0], skip_special_tokens=True)
+
+  #load gemnini for creating story
+  llm = ChatGoogleGenerativeAI(google_api_key=GEMINI_API_KEY, model=GEMINI_MODEL, temperature=0.3)
+
+  query =f' Write a 4 chapters story based on {model_prompt} and\
+  that fits the following requirements: {requirement}. Give a detailed\
+  description of the charaters appearences.'
+
+  result = llm.invoke(query)
+  story= result.content.replace('\n',' ')
+
+  # create promts for image gen from story
+  image_prompt_llm=ChatGoogleGenerativeAI(google_api_key=GEMINI_API_KEY, model=GEMINI_MODEL, temperature=0.3)
+
+  # create shemas to format output
+  schemas=[
+      ResponseSchema(name='prompt 1', description='the prompt'),
+      ResponseSchema(name='prompt 2', description='the prompt'),
+      ResponseSchema(name='prompt 3', description='the prompt'),
+      ResponseSchema(name='prompt 4', description='the prompt')
+  ]
+
+  # initialize parser for output
+  parser=StructuredOutputParser.from_response_schemas(schemas)
+  instructions=parser.get_format_instructions()
+
+  query = f' Based on this story: {story}. Create 4 prompts for stable diffusion that tells of a maximum of 77 tokens\
+  what happens in each chapters. Describe the characters everytime their name is mentioned. Each image should be created in the same exact style {style}.\
+  '+ '\n\n'+instructions
+
+  result=image_prompt_llm.invoke(query)
+  image_prompts = parser.parse(result.content)
+
+  # iterate through the prompts and generate new images
+  images=[]
+  for i in image_prompts.keys():
+
+    image = pipe(image_prompts[i]).images[0]
+    images.append(image)
+
+
+  return images, story
+
+# gradio
+interface = gr.Interface(
+    fn=image_story_generator,
+    inputs=[gr.Image(type='filepath'),gr.Textbox('enter story requirements'), gr.Textbox('pick a style for the images')],
+
+    outputs=[gr.Gallery(),
+        gr.Textbox('story')
+    ],
+    description='Upload an image to start the story generation process.'
+)
+
+interface.launch()

requirements.txt

@@ -0,0 +1,10 @@
+diffusers==0.32.2
+gradio==5.15.0
+huggingface_hub==0.26.2
+huggingface_hub==0.28.1
+langchain==0.3.18
+langchain_google_genai==2.0.9
+Pillow==11.1.0
+python-dotenv==1.0.1
+transformers==4.46.1
+transformers==4.48.3