add files

app.py

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+import os
+import io
+from PIL import Image
+import base64
+from dotenv import load_dotenv, find_dotenv
+import gradio as gr
+import requests,json
+
+_ = load_dotenv(find_dotenv())  # read local .env file
+
+hf_api_key = os.environ['HF_API_KEY']
+
+# Adjusted Helper function
+def get_completion(inputs, parameters=None, ENDPOINT_URL=os.environ['HF_API_TTI_BASE']):
+    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.post(ENDPOINT_URL, headers=headers, json=data)
+
+    if 'application/json' in response.headers.get('Content-Type'):
+        return response.json()  # If response is JSON
+    else:
+        # If response is not JSON, handle as binary (image data)
+        return base64.b64encode(response.content).decode('utf-8')  # Convert binary 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
+
+#Updated generate function to handle base64 image string
+def generate(prompt):
+    output = get_completion(prompt)
+    # Assuming output is now a base64 encoded string of the image
+    pil_image = base64_to_pil(output)  # Convert base64 string to PIL Image
+    return pil_image
+
+# Rest of your Gradio setup remains the same
+gr.close_all()
+demo = gr.Interface(fn=generate,
+                    inputs=[gr.Textbox(label="Your prompt")],
+                    outputs=[gr.Image(label="Result")],
+                    title="Image Generation with Stable Diffusion",
+                    description="Generate any image with Stable Diffusion",
+                    allow_flagging="never")
+demo.launch(share=True)