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LICENSE

@@ -0,0 +1,238 @@
+                         ACADEMIC PUBLIC LICENSE
+                                version 1.1
+
+               Copyright (c) 2024 MOHAMMAD ALBARHAM
+
+
+                                Preamble
+
+  This license contains the terms and conditions of using Arabic Story Generator in
+noncommercial settings: at academic institutions for teaching and research
+use and for personal or educational purposes. You will find that this
+license provides noncommercial users of Arabic Story Generator with rights that are
+similar to the well-known GNU General Public License, yet it retains the
+possibility for Arabic Story Generator authors to financially support the development by
+selling commercial licenses. In fact, if you intend to use Arabic Story Generator in a
+"for-profit" environment, where research is conducted to develop or enhance
+a product, is used in a commercial service offering, or when an entity uses
+Arabic Story Generator to participate in government-funded, EU-funded, military or similar
+research projects, then you need to obtain a commercial license (OMNEST).
+In that case, or if you are unsure, please contact the Author or visit
+www.omnest.com to inquire about commercial licenses.
+
+  What are the rights given to noncommercial users? Similarly to GPL, you
+have the right to use the software, to distribute copies, to receive source
+code, to change the software and distribute your modifications or the
+modified software. Also similarly to the GPL, if you distribute verbatim or
+modified copies of this software, they must be distributed under this
+license.
+
+  By modeling the GPL, this license guarantees that you're safe when using
+Arabic Story Generator in your work, for teaching or research. This license guarantees
+that Arabic Story Generator will remain available free of charge for nonprofit use. You
+can modify Arabic Story Generator to your purposes, and you can also share your modifications.
+Even in the unlikely case of the authors abandoning Arabic Story Generator entirely, this
+license permits anyone to continue developing it from the last release, and
+to create further releases under this license.
+
+  We believe that the combination of noncommercial open-source and commercial
+licensing will be beneficial for the whole user community, because income from
+commercial licenses will enable faster development and a higher level of
+software quality, while further enjoying the informal, open communication
+and collaboration channels of open source development.
+
+  The precise terms and conditions for using, copying, distribution and
+modification follow.
+
+
+                         ACADEMIC PUBLIC LICENSE
+
+    TERMS AND CONDITIONS FOR USE, COPYING, DISTRIBUTION AND MODIFICATION
+
+  0. Definitions
+
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+this Academic Public License.
+
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+under copyright law: that is to say, a work containing the Program or a
+portion of it, either verbatim or with modifications and/or translated into
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+
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+tools that are part of the Program, or creating works based on the Program.
+
+Each licensee is addressed as "you".
+
+  1. Permission is hereby granted to use the Program free of charge for
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+research projects, you have to contact the Author or visit www.omnest.com
+for an appropriate license. Permission is also granted to use the Program
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+usefulness for a particular purpose.
+
+  2. You may copy and distribute verbatim copies of the Program's
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+along with the Program.
+
+  3. You may modify your copy or copies of the Program or any portion
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+
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+
+These requirements apply to the modified work as a whole.  If
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+
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+
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+
+  5. You may not copy, modify, sublicense, or distribute the Program
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+
+  6. You are not required to accept this License, since you have not
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+
+  7. Each time you redistribute the Program (or any work based on the
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+
+  8. If, as a consequence of a court judgment or allegation of patent
+infringement or for any other reason (not limited to patent issues),
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+otherwise) that contradict the conditions of this License, they do not
+excuse you from the conditions of this License.  If you cannot
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+may not distribute the Program at all.  For example, if a patent
+license would not permit royalty-free redistribution of the Program by
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+
+If any portion of this section is held invalid or unenforceable under
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+apply and the section as a whole is intended to apply in other
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+
+  9. If the distribution and/or use of the Program are restricted in
+certain countries either by patents or by copyrighted interfaces, the
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+
+                            NO WARRANTY
+
+  10. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
+FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW.  EXCEPT WHEN
+OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
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+TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU.  SHOULD THE
+PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
+REPAIR OR CORRECTION.
+
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+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
+REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
+INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
+OUT OF THE USE OR INABILITY TO USE THE PROGRAM INCLUDING BUT NOT LIMITED
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+PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
+POSSIBILITY OF SUCH DAMAGES.
+
+                     END OF TERMS AND CONDITIONS
+

README.md

@@ -1,13 +1,51 @@
----
-title: Arabic Story Generator
-emoji: 🦀
-colorFrom: purple
-colorTo: blue
-sdk: gradio
-sdk_version: 4.32.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Gradio Application
+
+<img src="image_logo.png" style="width:50%; height:auto;">
+
+
+This is a Gradio application that allows you to generate an Arabic story using generative AI models.
+
+## Installation
+
+1. Clone this repository:
+
+    ```shell
+    git clone https://github.com/mohammad-albarham/Arabic_story_generator.git
+    ```
+
+2. Install the required dependencies:
+
+    ```shell
+    pip install -r requirements.txt
+    ```
+
+## Usage
+
+0. Add the keys for OPEN AI API model and stability AI API in [models.py](https://github.com/mohammad-albarham/Arabic_story_generator/blob/3702d6cad85fe38ff5944d7f99f43a37d7dec151/llm_models.py#L16) and [image_generator.py](https://github.com/mohammad-albarham/Arabic_story_generator/blob/3702d6cad85fe38ff5944d7f99f43a37d7dec151/image_generator.py#L22)
+1. Run the application:
+
+    ```shell
+    gradio app.py
+    ```
+
+2. Open your web browser and navigate to [http://localhost:7860](http://localhost:7860).
+
+3. Add your a description and the needed number of pages and click on generate story.
+
+## Contributing
+
+Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.
+
+### Instrcutions for the contribution:
+
+1. Please install black formatter as follows:
+`pip install black`
+2. Make sure to format all python files you want to change using this command on the terminal:
+`black .`
+
+You can see this tutorial for more information about the formatter: [tutorial](https://www.freecodecamp.org/news/auto-format-your-python-code-with-black/)
+
+
+## License
+
+[ACADEMIC PUBLIC LICENSE](https://github.com/mohammad-albarham/Arabic_story_generator/tree/main?tab=License-1-ov-file)

app.py

@@ -3,7 +3,7 @@ from llm_models import get_text_image_pairs
 import time
 from tqdm import tqdm
 
-title_markdown = ("""
+title_markdown = """
 <div style="display: flex; justify-content: center; align-items: center; text-align: center; direction: rtl;">
   <img src="https://s11.ax1x.com/2023/12/28/piqvDMV.png" alt="MoE-LLaVA🚀" style="max-width: 120px; height: auto; margin-right: 20px;">
   <div style="display: flex; flex-direction: column; justify-content: center; align-items: center;">
@@ -12,15 +12,19 @@ title_markdown = ("""
     <h2 style="margin: 0; font-size: 1.5em;">صانع القصص بالذكاء الاصطناعي التوليدي</h2>
   </div>
 </div>
-""")
+"""
+
 
-def get_text_images_values(k, input_prompt):
+def get_text_images_values(k, input_prompt, api_key_openai, api_key_stability_ai):
 
     pages = int(k)
+    segments_list, images_names = get_text_image_pairs(
+        pages, input_prompt, api_key_openai, api_key_stability_ai
+    )
 
-    segments_list, images_names =  get_text_image_pairs(pages,input_prompt)
     return segments_list, images_names
 
+
 css = """
 .gradio-container {direction: rtl}
 .gradio-container-4-18-0 .prose h1 {direction: rtl};
@@ -31,30 +35,61 @@ with gr.Blocks(css=css) as demo:
 
     gr.Markdown(title_markdown)
 
-    prompt = gr.Textbox(label="معلومات بسيطة عن القصة",
-                info="أدخل بعض المعلومات عن القصة، مثلاً: خالد صبي في الرابعة من عمره، ويحب أن يصبح طياراً في المستقبل",
-                placeholder="خالد صبي في الرابعة من عمره، ويحب أن يصبح طياراً في المستقبل",
-                text_align="right",
-                rtl=True,
-                elem_classes="rtl-textbox",
-                elem_id="rtl-textbox")
-
+    with gr.Row():
+        api_key_openai = gr.Textbox(
+            label="Open AI API Key",
+            placeholder="أدخل مفتاح API الخاص بك هنا",
+            type="password",
+        )
+        api_key_stability_ai = gr.Textbox(
+            label="Stability AI API Key",
+            placeholder="أدخل مفتاح API الخاص بك هنا",
+            type="password",
+        )
+
+    prompt = gr.Textbox(
+        label="معلومات بسيطة عن القصة",
+        info="أدخل بعض المعلومات عن القصة، مثلاً: خالد صبي في الرابعة من عمره، ويحب أن يصبح طياراً في المستقبل",
+        placeholder="خالد صبي في الرابعة من عمره، ويحب أن يصبح طياراً في المستقبل",
+        text_align="right",
+        rtl=True,
+        elem_classes="rtl-textbox",
+        elem_id="rtl-textbox",
+    )
 
     with gr.Row():
-        
 
-        max_textboxes = 10 # Define the max number of textboxed, so we will add the max number of textboxes and images to the layout
+        max_textboxes = 10  # Define the max number of textboxed, so we will add the max number of textboxes and images to the layout
 
         def variable_outputs(k, segments_list):
             k = int(k)
-            return [gr.Textbox(label= f"الصفحة رقم {i+1}", value=item, text_align="right", visible=True) for i, item in enumerate(segments_list)] + [gr.Textbox(visible=False, text_align="right", rtl=True)]*(max_textboxes-k)
-
-        def variable_outputs_image(k,images_names):
+            return [
+                gr.Textbox(
+                    label=f"الصفحة رقم {i+1}",
+                    value=item,
+                    text_align="right",
+                    visible=True,
+                )
+                for i, item in enumerate(segments_list)
+            ] + [gr.Textbox(visible=False, text_align="right", rtl=True)] * (
+                max_textboxes - k
+            )
+
+        def variable_outputs_image(k, images_names):
             k = int(k)
-            return [gr.Image(value=item, scale=1, visible=True) for item in images_names] + [gr.Image(scale=1,visible=False)]*(max_textboxes-k)
-    
+            return [
+                gr.Image(value=item, scale=1, visible=True) for item in images_names
+            ] + [gr.Image(scale=1, visible=False)] * (max_textboxes - k)
+
         with gr.Column():
-            s = gr.Slider(1, max_textboxes, value=1, step=1, info="أقصى عدد صفحات يمكن توليده هو 10 صفحات",label="كم عدد صفحات القصة التي تريدها؟")
+            s = gr.Slider(
+                1,
+                max_textboxes,
+                value=1,
+                step=1,
+                info="أقصى عدد صفحات يمكن توليده هو 10 صفحات",
+                label="كم عدد صفحات القصة التي تريدها؟",
+            )
             textboxes = []
             imageboxes = []
             for i in tqdm(range(max_textboxes)):
@@ -64,15 +99,15 @@ with gr.Blocks(css=css) as demo:
                     imageboxes.append(i_t)
                     textboxes.append(t)
 
-            segment_list = gr.JSON(value=[],visible=False)
+            segment_list = gr.JSON(value=[], visible=False)
             images_list = gr.JSON(value=[], visible=False)
 
     submit = gr.Button(value="أنشئ القصة الآن")
 
     submit.click(
         fn=get_text_images_values,
-        inputs=[s,prompt],
-        outputs=[segment_list, images_list]
+        inputs=[s, prompt, api_key_openai, api_key_stability_ai],
+        outputs=[segment_list, images_list],
     ).then(
         fn=variable_outputs,
         inputs=[s, segment_list],
@@ -83,4 +118,4 @@ with gr.Blocks(css=css) as demo:
         outputs=imageboxes,
     )
 
-demo.launch()
+demo.launch()

image_generator.py

@@ -5,68 +5,73 @@ from PIL import Image
 from stability_sdk import client
 import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation
 import uuid
-
-
+import gradio as gr
 
 # Our Host URL should not be prepended with "https" nor should it have a trailing slash.
-os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443'
-
-# Sign up for an account at the following link to get an API Key.
-# https://platform.stability.ai/
-
-# Click on the following link once you have created an account to be taken to your API Key.
-# https://platform.stability.ai/account/keys
+os.environ["STABILITY_HOST"] = "grpc.stability.ai:443"
 
-# Paste your API Key below.
 
-os.environ['STABILITY_KEY'] = 'key_here'
+def get_image(prompt, api_key_stability_ai):
 
+    # Sign up for an account at the following link to get an API Key.
+    # https://platform.stability.ai/
+    # Click on the following link once you have created an account to be taken to your API Key.
+    # https://platform.stability.ai/account/keys
 
+    # Set up our connection to the API.
+    if api_key_stability_ai == "":
+        raise gr.Error("Please add your Stability AI API key ")
+    else:
+        try:
+            stability_api = client.StabilityInference(
+                key=api_key_stability_ai,  # API Key reference.
+                verbose=True,  # Print debug messages.
+                engine="stable-diffusion-xl-1024-v1-0",  # Set the engine to use for generation.
+                # Check out the following link for a list of available engines: https://platform.stability.ai/docs/features/api-parameters#engine
+            )
 
-# Set up our connection to the API.
-stability_api = client.StabilityInference(
-    key=os.environ['STABILITY_KEY'], # API Key reference.
-    verbose=True, # Print debug messages.
-    engine="stable-diffusion-xl-1024-v1-0", # Set the engine to use for generation.
-    # Check out the following link for a list of available engines: https://platform.stability.ai/docs/features/api-parameters#engine
-)
+            # Set up our initial generation parameters.
+            answers = stability_api.generate(
+                prompt=prompt,  # The prompt we want to generate an image from.
+                seed=4253978046,  # If a seed is provided, the resulting generated image will be deterministic.
+                # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
+                # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
+                steps=30,  # Amount of inference steps performed on image generation. Defaults to 30.
+                cfg_scale=8.0,  # Influences how strongly your generation is guided to match your prompt.
+                # Setting this value higher increases the strength in which it tries to match your prompt.
+                # Defaults to 7.0 if not specified.
+                width=512,  # Generation width, defaults to 512 if not included.
+                height=512,  # Generation height, defaults to 512 if not included.
+                samples=1,  # Number of images to generate, defaults to 1 if not included.
+                sampler=generation.SAMPLER_K_DPMPP_2M,  # Choose which sampler we want to denoise our generation with.
+                # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
+                # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m, k_dpmpp_sde)
+            )
 
-def get_image(prompt):
-    # Set up our initial generation parameters.
-    answers = stability_api.generate(
-        prompt=prompt, # The prompt we want to generate an image from.
-        seed=4253978046, # If a seed is provided, the resulting generated image will be deterministic.
-                        # What this means is that as long as all generation parameters remain the same, you can always recall the same image simply by generating it again.
-                        # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
-        steps=30, # Amount of inference steps performed on image generation. Defaults to 30. 
-        cfg_scale=8.0, # Influences how strongly your generation is guided to match your prompt.
-                    # Setting this value higher increases the strength in which it tries to match your prompt.
-                    # Defaults to 7.0 if not specified.
-        width=512, # Generation width, defaults to 512 if not included.
-        height=512, # Generation height, defaults to 512 if not included.
-        samples=1, # Number of images to generate, defaults to 1 if not included.
-        sampler=generation.SAMPLER_K_DPMPP_2M # Choose which sampler we want to denoise our generation with.
-                                                    # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
-                                                    # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m, k_dpmpp_sde)
-    )
+            # print("Finish the prompt")
+            # Set up our warning to print to the console if the adult content classifier is tripped.
+            # If adult content classifier is not tripped, save generated images.
+            for resp in answers:
+                for artifact in resp.artifacts:
+                    # if artifact.finish_reason == generation.FILTER:
+                    #     print(artifact.finish_reason)
+                    #     print("Warning")
+                    #     warnings.warn(
+                    #         "Your request activated the API's safety filters and could not be processed."
+                    #         "Please modify the prompt and try again.")
 
-    # print("Finish the prompt")
-    # Set up our warning to print to the console if the adult content classifier is tripped.
-    # If adult content classifier is not tripped, save generated images.
-    for resp in answers:
-        for artifact in resp.artifacts:
-            # if artifact.finish_reason == generation.FILTER:
-            #     print(artifact.finish_reason)
-            #     print("Warning")
-            #     warnings.warn(
-            #         "Your request activated the API's safety filters and could not be processed."
-            #         "Please modify the prompt and try again.")
-                
-            if artifact.type == generation.ARTIFACT_IMAGE:
-                img = Image.open(io.BytesIO(artifact.binary))
-                unique_filename = str(uuid.uuid4())
+                    if artifact.type == generation.ARTIFACT_IMAGE:
+                        img = Image.open(io.BytesIO(artifact.binary))
+                        unique_filename = str(uuid.uuid4())
 
-                img.save(str(unique_filename)+ ".png") # Save our generated images with their seed number as the filename.
+                        img.save(
+                            str(unique_filename) + ".png"
+                        )  # Save our generated images with their seed number as the filename.
 
-    return unique_filename + ".png"
+            return unique_filename + ".png"
 
+        except Exception as error:
+            print(str(error))
+            raise gr.Error(
+                "An error occurred while generating the image. Please try again."
+            )

llm_models.py

@@ -3,126 +3,157 @@ from openai import OpenAI
 from pydantic import BaseModel
 from typing import List
 from image_generator import get_image
+import gradio as gr
+
 
 class StepByStepAIResponse(BaseModel):
     title: str
     story_segments: List[str]
     image_prompts: List[str]
 
+
 class GetTranslation(BaseModel):
     translated_text: List[str]
 
 
-client = OpenAI(api_key="key_here")
-
-
-def generate_story(k, prompt):
-        """ Generate a story with k segments and initial prompt"""
-
-        response = client.chat.completions.create(
-        model="gpt-4-turbo-preview",
-        messages=[
-            {
-            "role": "system",
-            "content": f"""
-            Your expertise lies in weaving captivating narratives for children, complemented by images that vividly bring each tale to life. Embark on a creative endeavor to construct a story segmented into {k} distinct chapters, each a cornerstone of an enchanting journey for a young audience.
-            The input prompt will be on Arabic, but the output must be in English.
-
-            **Task Overview**:
-
-            1. **Story Development**:
-            - Craft a narrative divided into {k} parts, with a strict 50-word limit for each.
-            - Start with an engaging introduction that lays the foundation for the adventure.
-            - Ensure each part naturally progresses from the previous, crafting a fluid story that escalates to an exhilarating climax.
-            - Wrap up the narrative with a gratifying conclusion that ties all story threads together.
-            - Keep character continuity intact across the story, with consistent presence from beginning to end.
-            - You must describe the characters in details in every image prompt.
-            - Use language and themes that are child-friendly, imbued with wonder, and easy to visualize.
-            - The story will talk about {prompt}
-
-            2. **Image Generation Instructions for Image Models**:
-            - For every story part, create a comprehensive prompt for generating an image that encapsulates the scene's essence. Each prompt should:
-                - Offer a detailed description of the scene, characters, and critical elements, providing enough specificity for the image model to create a consistent and coherent visual.
-                - Request the images be in an anime style to ensure visual consistency throughout.
-                - Given the image model's isolated processing, reintroduce characters, settings, and pivotal details in each prompt to maintain narrative and visual continuity.
-                - Focus on visual storytelling components that enhance the story segments, steering clear of direct text inclusion in the images.
-
-            **Key Points**:
-            - Due to the image model's lack of recall, stress the need for self-contained prompts that reintroduce crucial elements each time. This strategy guarantees that, although generated independently, each image mirrors a continuous and cohesive visual story.
-
-            Through your skill in melding textual and visual storytelling, you will breathe life into this magical tale, offering young readers a journey to remember through both prose and illustration.
-
-            """
-            },
-        ],
-        functions=[
-            {
-                "name": "get_story_segments_and_image_prompts",
-                "description": "Get user answer in series of segment and image prompts",
-                "parameters": StepByStepAIResponse.model_json_schema(),
-            }
-        ],
-        function_call={"name": "get_story_segments_and_image_prompts"},  # Corrected to match the defined function name
-        temperature=1,
-        max_tokens=1000,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
-        )
-
-        output = json.loads(response.choices[0].message.function_call.arguments)
-
-        sbs = StepByStepAIResponse(**output)
-
-        return sbs
-    
-def get_Arabic_translation(story_segments):
-        
-        response = client.chat.completions.create(
-        model="gpt-4-turbo-preview",
-        messages=[
-            {
-            "role": "system",
-            "content": 
-            
-            f"""
-            You are an expert translator of text from English to Arabic.
-
-            On the following, you can find the input text that you need to translate to Arabic: 
-            {story_segments}
-
-            Translate it from English to Arabic.
-
-            """
-            },
-        ],
-        functions=[
-            {
-                "name": "translate_text_from_english_to_arabic",
-                "description": "Translate the text from English to Arabic.",
-                "parameters": GetTranslation.model_json_schema(),
-            }
-        ],
-        function_call={"name": "translate_text_from_english_to_arabic"},  # Corrected to match the defined function name
-        temperature=1,
-        max_tokens=1000,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
-        )
-
-        output = json.loads(response.choices[0].message.function_call.arguments)
-
-        sbs = GetTranslation(**output)
-
-        return sbs
-
-def get_text_image_pairs(k, prompt):
-
-    describtion = generate_story(k, prompt)
-
-    segements_translation = get_Arabic_translation(describtion.story_segments)
-
-    images_names = [get_image(itm) for itm in describtion.image_prompts]
-
-    return (segements_translation.translated_text, images_names)
+def generate_story(k, prompt, api_key):
+    """Generate a story with k segments and initial prompt"""
+
+    if api_key == "":
+        raise gr.Error("Please add your OpenAI API key ")
+
+    else:
+        try:
+            client = OpenAI(api_key=api_key)
+
+            response = client.chat.completions.create(
+                model="gpt-4-turbo-preview",
+                messages=[
+                    {
+                        "role": "system",
+                        "content": f"""
+                    Your expertise lies in weaving captivating narratives for children, complemented by images that vividly bring each tale to life. Embark on a creative endeavor to construct a story segmented into {k} distinct chapters, each a cornerstone of an enchanting journey for a young audience.
+                    The input prompt will be on Arabic, but the output must be in English.
+
+                    **Task Overview**:
+
+                    1. **Story Development**:
+                    - Craft a narrative divided into {k} parts, with a strict 50-word limit for each.
+                    - Start with an engaging introduction that lays the foundation for the adventure.
+                    - Ensure each part naturally progresses from the previous, crafting a fluid story that escalates to an exhilarating climax.
+                    - Wrap up the narrative with a gratifying conclusion that ties all story threads together.
+                    - Keep character continuity intact across the story, with consistent presence from beginning to end.
+                    - You must describe the characters in details in every image prompt.
+                    - Use language and themes that are child-friendly, imbued with wonder, and easy to visualize.
+                    - The story will talk about {prompt}
+
+                    2. **Image Generation Instructions for Image Models**:
+                    - For every story part, create a comprehensive prompt for generating an image that encapsulates the scene's essence. Each prompt should:
+                        - Offer a detailed description of the scene, characters, and critical elements, providing enough specificity for the image model to create a consistent and coherent visual.
+                        - Request the images be in an anime style to ensure visual consistency throughout.
+                        - Given the image model's isolated processing, reintroduce characters, settings, and pivotal details in each prompt to maintain narrative and visual continuity.
+                        - Focus on visual storytelling components that enhance the story segments, steering clear of direct text inclusion in the images.
+
+                    **Key Points**:
+                    - Due to the image model's lack of recall, stress the need for self-contained prompts that reintroduce crucial elements each time. This strategy guarantees that, although generated independently, each image mirrors a continuous and cohesive visual story.
+
+                    Through your skill in melding textual and visual storytelling, you will breathe life into this magical tale, offering young readers a journey to remember through both prose and illustration.
+
+                    """,
+                    },
+                ],
+                functions=[
+                    {
+                        "name": "get_story_segments_and_image_prompts",
+                        "description": "Get user answer in series of segment and image prompts",
+                        "parameters": StepByStepAIResponse.model_json_schema(),
+                    }
+                ],
+                function_call={
+                    "name": "get_story_segments_and_image_prompts"
+                },  # Corrected to match the defined function name
+                temperature=1,
+                max_tokens=1000,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+            )
+
+            output = json.loads(response.choices[0].message.function_call.arguments)
+
+            sbs = StepByStepAIResponse(**output)
+
+            return sbs
+
+        except Exception as error:
+            print(str(error))
+            raise gr.Error(
+                "An error occurred while generating the story. Please try again."
+            )
+
+
+def get_Arabic_translation(story_segments, api_key):
+
+    if api_key == "":
+        raise gr.Error("Please add your OpenAI API key ")
+
+    else:
+        try:
+            client = OpenAI(api_key=api_key)
+            response = client.chat.completions.create(
+                model="gpt-4-turbo-preview",
+                messages=[
+                    {
+                        "role": "system",
+                        "content": f"""
+                        You are an expert translator of text from English to Arabic.
+
+                        On the following, you can find the input text that you need to translate to Arabic: 
+                        {story_segments}
+
+                        Translate it from English to Arabic.
+
+                        """,
+                    },
+                ],
+                functions=[
+                    {
+                        "name": "translate_text_from_english_to_arabic",
+                        "description": "Translate the text from English to Arabic.",
+                        "parameters": GetTranslation.model_json_schema(),
+                    }
+                ],
+                function_call={
+                    "name": "translate_text_from_english_to_arabic"
+                },  # Corrected to match the defined function name
+                temperature=1,
+                max_tokens=1000,
+                top_p=1,
+                frequency_penalty=0,
+                presence_penalty=0,
+            )
+            output = json.loads(response.choices[0].message.function_call.arguments)
+
+            sbs = GetTranslation(**output)
+
+            return sbs
+        except Exception as error:
+            print(str(error))
+            raise gr.Error(
+                "An error occurred while translating the text. Please try again."
+            )
+
+
+def get_text_image_pairs(k, prompt, api_key_openai, api_key_stability_ai):
+
+    describtion = generate_story(k, prompt, api_key_openai)
+
+    segements_translation = get_Arabic_translation(
+        describtion.story_segments, api_key_openai
+    )
+
+    images_names = [
+        get_image(itm, api_key_stability_ai) for itm in describtion.image_prompts
+    ]
+
+    return (segements_translation.translated_text, images_names)

requirements.txt

@@ -4,3 +4,4 @@ openai==1.12.0
 pydantic==2.6.1
 rich==13.7.0
 stability-sdk==0.8.5
+black==24.4.2