Update app.py

app.py

@@ -1,38 +1,83 @@
-import gradio as gr
+from flask import Flask, request, jsonify, send_file
+from flask_cors import CORS
+import os
 from huggingface_hub import InferenceClient
+from io import BytesIO
 from PIL import Image
-import io
-
-# Initialize the Inference Client
-client = InferenceClient()
-
-# Function to handle the image generation
-def generate_image(image, prompt):
-    # Open the uploaded image
-    image = Image.open(image)
-
-    # Generate image using the InferenceClient
-    generated_image = client.image_to_image(image, prompt=prompt)
-
-    # Save generated image to a BytesIO object
-    img_byte_arr = io.BytesIO()
-    generated_image.save(img_byte_arr, format='PNG')
-    img_byte_arr.seek(0)  # Move the cursor to the beginning
-
-    # Return the generated image
-    return generated_image
-
-# Create Gradio interface
-iface = gr.Interface(
-    fn=generate_image,
-    inputs=[
-        gr.inputs.Image(type="file", label="Input Image"),  # File input for uploaded image
-        gr.inputs.Textbox(label="Prompt")  # Textbox for the prompt
-    ],
-    outputs=gr.outputs.Image(type="pil", label="Generated Image"),  # Output as PIL image
-    title="Image Generation with Hugging Face",
-    description="Upload an image and provide a prompt to generate a new image."
-)
-
-# Launch the Gradio interface
-iface.launch()
+
+# Initialize the Flask app
+app = Flask(__name__)
+CORS(app)  # Enable CORS for all routes
+
+# Initialize the InferenceClient with your Hugging Face token
+HF_TOKEN = os.environ.get("HF_TOKEN")  # Ensure to set your Hugging Face token in the environment
+client = InferenceClient(token=HF_TOKEN)
+
+@app.route('/')
+def home():
+    return "Welcome to the Image Background Remover!"
+
+
+# Function to generate an image from a text prompt
+def generate_image(prompt, negative_prompt=None, height=512, width=512, model="stabilityai/stable-diffusion-2-1", num_inference_steps=50, guidance_scale=7.5, seed=None):
+    try:
+        # Generate the image using Hugging Face's inference API with additional parameters
+        image = client.text_to_image(
+            prompt=prompt, 
+            negative_prompt=negative_prompt, 
+            height=height, 
+            width=width, 
+            model=model,
+            num_inference_steps=num_inference_steps,  # Control the number of inference steps
+            guidance_scale=guidance_scale,  # Control the guidance scale
+            seed=seed  # Control the seed for reproducibility
+        )
+        return image  # Return the generated image
+    except Exception as e:
+        print(f"Error generating image: {str(e)}")
+        return None
+
+# Flask route for the API endpoint to generate an image
+@app.route('/generate_image', methods=['POST'])
+def generate_api():
+    data = request.get_json()
+
+    # Extract required fields from the request
+    prompt = data.get('prompt', '')
+    negative_prompt = data.get('negative_prompt', None)
+    height = data.get('height', 1024)  # Default height
+    width = data.get('width', 720)  # Default width
+    num_inference_steps = data.get('num_inference_steps', 50)  # Default number of inference steps
+    guidance_scale = data.get('guidance_scale', 7.5)  # Default guidance scale
+    model_name = data.get('model', 'stabilityai/stable-diffusion-2-1')  # Default model
+    seed = data.get('seed', None)  # Seed for reproducibility, default is None
+
+    if not prompt:
+        return jsonify({"error": "Prompt is required"}), 400
+
+    try:
+        # Call the generate_image function with the provided parameters
+        image = generate_image(prompt, negative_prompt, height, width, model_name, num_inference_steps, guidance_scale, seed)
+
+        if image:
+            # Save the image to a BytesIO object
+            img_byte_arr = BytesIO()
+            image.save(img_byte_arr, format='PNG')  # Convert the image to PNG
+            img_byte_arr.seek(0)  # Move to the start of the byte stream
+
+            # Send the generated image as a response
+            return send_file(
+                img_byte_arr, 
+                mimetype='image/png', 
+                as_attachment=False,  # Send the file as an attachment
+                download_name='generated_image.png'  # The file name for download
+            )
+        else:
+            return jsonify({"error": "Failed to generate image"}), 500
+    except Exception as e:
+        print(f"Error in generate_api: {str(e)}")  # Log the error
+        return jsonify({"error": str(e)}), 500
+
+# Add this block to make sure your app runs when called
+if __name__ == "__main__":
+    app.run(host='0.0.0.0', port=7860)  # Run directly if needed for testing