Upload app.py

app.py

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+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)