Delete app.py

app.py

@@ -1,94 +0,0 @@
-import os
-import io
-#import IPython.display
-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']
-
-# Helper function
-import requests, json
-import gradio as gr
-
-#A helper function to convert the PIL image to base64 
-# so you can send it to the API
-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 generate(prompt, negative_prompt, steps, guidance, width, height):
-    params = {
-        "negative_prompt": negative_prompt,
-        "num_inference_steps": steps,
-        "guidance_scale": guidance,
-        "width": width,
-        "height": height
-    }
-    
-    end_point_url="https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
-    output = get_completion(prompt, params,end_point_url)
-    pil_image = base64_to_pil(output)
-    return pil_image
-
-#Text-to-image endpoint
-def get_completion(inputs, parameters=None, end_point_url=None):
-    headers = {
-      "Authorization": f"Bearer {hf_api_key}",
-      "Content-Type": "application/json"
-    }   
-    data = { "inputs": inputs }
-    if parameters is not None:
-        data.update({"parameters": parameters})
-    response = requests.request("POST",
-                                end_point_url,
-                                headers=headers,
-                                data=json.dumps(data))
-    print ("response:",response)
-    print("response.content:",response.content)
-    
-    return json.loads(response.content.decode("utf-8"))
-
-
-API_URL = "https://api-inference.huggingface.co/models/runwayml/stable-diffusion-v1-5"
-headers = {"Authorization": "Bearer "+hf_api_key}
-
-
-#nel corso usa chiamata a generate che puntava ad un endpoit diverso. se usa quella chiamata  non va (incompatibilità endpoint-funzione?)
-# questa è identica a quella suggerita dal sito Use this model with the Inference API. profata in simple_api_call.py
-def my_generate(payload, negative_prompt, steps, guidance, width, height):
-    payload_input={"inputs": payload}
-    #print("payload:",payload_input)
-    response= requests.post(API_URL, headers=headers, json=payload_input)
-    #print("response.content:",response.content)
-    image = Image.open(io.BytesIO(response.content))
-    return image
-
- 
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Image Generation with Stable Diffusion")
-    with gr.Row():
-        with gr.Column(scale=4):
-            prompt = gr.Textbox(label="Your prompt") #Give prompt some real estate
-        with gr.Column(scale=1, min_width=50):
-            btn = gr.Button("Submit") #Submit button side by side!
-    with gr.Accordion("Advanced options", open=False): #Let's hide the advanced options!
-            negative_prompt = gr.Textbox(label="Negative prompt")
-            with gr.Row():
-                with gr.Column():
-                    steps = gr.Slider(label="Inference Steps", minimum=1, maximum=100, value=25,
-                      info="In 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=512, step=64, value=512)
-                    height = gr.Slider(label="Height", minimum=64, maximum=512, step=64, value=512)
-    output = gr.Image(label="Result") #Move the output up too
-            
-    btn.click(fn=my_generate, inputs=[prompt,negative_prompt,steps,guidance,width,height], outputs=[output])
-
-
-demo.launch(share=False, server_port=8081)