Upload folder using huggingface_hub

.github/workflows/update_space.yml

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+name: Run Python script
+
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,12 +1,6 @@
 ---
-title: Image Gen Tr
-emoji: ⚡
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.43.0
+title: image_gen_tr
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.41.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+import io
+import requests, json
+from PIL import Image
+import base64
+from dotenv import load_dotenv, find_dotenv
+
+_ = load_dotenv(find_dotenv()) # read local .env file
+hf_api_key = os.environ['HF_API_KEY']
+
+# Load the translation model (Turkish to English)
+API_URL = "https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-tr-en"
+
+headers = {
+        "Authorization": f"Bearer {hf_api_key}",
+        "Content-Type": "application/json"
+    }
+
+# Text-to-image endpoint
+def get_completion(inputs, parameters=None, ENDPOINT_URL=os.environ['HF_API_TTI_STABILITY_AI']):
+    data = {"inputs": inputs}
+    if parameters is not None:
+        data.update({"parameters": parameters})
+
+    response = requests.post(ENDPOINT_URL, headers=headers, data=json.dumps(data))
+    
+    # Check the content type of the response
+    content_type = response.headers.get('Content-Type', '')
+    print(content_type)
+    if 'application/json' in content_type:
+        return json.loads(response.content.decode("utf-8"))
+    elif 'image/' in content_type:
+        return response.content  # return raw image data
+
+    response.raise_for_status()  # raise an error for unexpected content types
+
+# A helper function to convert the PIL image to base64 
+def base64_to_pil(img_base64):
+    base64_decoded = base64.b64decode(img_base64)
+    byte_stream = io.BytesIO(base64_decoded)
+    pil_image = Image.open(byte_stream)
+    return pil_image
+
+def query(payload):
+	response = requests.post(API_URL, headers=headers, json=payload)
+	return response.json()
+
+# Translation function
+def translate_to_english(text):
+    try:
+        # Translate the input from Turkish to English
+        translation = query({"inputs": text})
+        print(translation)
+        translated_text = translation[0]['translation_text']
+        return translated_text
+    except Exception as e:
+        print(f"Translation error: {e}")
+        return text  # If translation fails, return original text
+
+# Main generation function with translation
+def generate(prompt, negative_prompt, steps, guidance, width, height):
+    # Translate the prompt to English if it's in Turkish
+    translated_prompt = translate_to_english(prompt)
+    print(f"Translated Prompt: {translated_prompt}")
+    
+    params = {
+        "negative_prompt": negative_prompt,
+        "num_inference_steps": steps,
+        "guidance_scale": guidance,
+        "width": width,
+        "height": height
+    }
+    
+    output = get_completion(translated_prompt, params)
+    
+    # Check if the output is an image (bytes) or JSON (dict)
+    if isinstance(output, dict):
+        raise ValueError("Expected an image but received JSON: {}".format(output))
+    
+    # If output is raw image data, convert it to a PIL image
+    result_image = Image.open(io.BytesIO(output))
+    return (translated_prompt, result_image)
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Image Generation with Turkish Inputs")
+    gr.Markdown("### [`stabilityai/stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) and [`Helsinki-NLP/opus-mt-tr-en`](https://huggingface.co/Helsinki-NLP/opus-mt-tr-en) models work under the hood!")
+    
+    with gr.Row():
+        with gr.Column(scale=4):
+            prompt = gr.Textbox(label="Your prompt (in Turkish or English)") # Accept Turkish or English input
+        with gr.Column(scale=1, min_width=50):
+            btn = gr.Button("Submit")
+    
+    with gr.Accordion("Advanced options", open=False):
+        negative_prompt = gr.Textbox(label="Negative prompt")
+        with gr.Row():
+            with gr.Column():
+                steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=50,
+                                  info="In how many steps will the denoiser denoise the image?")
+                guidance = gr.Slider(label="Guidance Scale", minimum=1, maximum=20, value=7,
+                                     info="Controls how much the text prompt influences the result")
+            with gr.Column():
+                width = gr.Slider(label="Width", minimum=64, maximum=1024, step=64, value=512)
+                height = gr.Slider(label="Height", minimum=64, maximum=1024, step=64, value=512)
+    translated_text = gr.Textbox(label="Translated text")
+    output = gr.Image(label="Result")
+            
+    btn.click(fn=generate, inputs=[prompt, negative_prompt, steps, guidance, width, height], outputs=[translated_text, output])
+
+gr.close_all()
+demo.launch(share=True)

requirements.txt

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+gradio
+pillow
+python-dotenv