Upgrading gradio app

.gitignore

@@ -0,0 +1 @@
+.venv/

app.py

@@ -2,79 +2,32 @@ import gradio as gr
 import requests
 
 # URL of the API created with FastAPI
-API_URL = "https://mlops-lab2-frdj.onrender.com"
+API_URL = "https://lab3-nuj8.onrender.com"
 
-# Function to predict image class
-def predict_image(image, class_names):
+# Function to execute when clicking the "Predict button"
+def predict(image):
     try:
-        # Save image to bytes
-        import io
-        from PIL import Image
+        image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
         
-        img_byte_arr = io.BytesIO()
-        image.save(img_byte_arr, format='PNG')
-        img_byte_arr.seek(0)
-        
-        # Prepare the request
-        files = {"file": ("image.png", img_byte_arr, "image/png")}
-        data = {"class_names": class_names}
-        
-        response = requests.post(f"{API_URL}/predict", files=files, data=data, timeout=10)
-        response.raise_for_status()
-        result = response.json()
-        return result.get("predicted_class")
-    except requests.exceptions.HTTPError as e:
-        return f"Error: {response.json().get('detail', str(e))}"
-    except Exception as e:
-        return f"Error: {str(e)}"
+        _, img_encoded = cv2.imencode(".jpg", image_bgr)
+        files = {"file": ("image.jpg", img_encoded.tobytes(), "image/jpeg")}
 
-# Function to resize image
-def resize_image(image, width, height):
-    try:
-        import io
-        
-        img_byte_arr = io.BytesIO()
-        image.save(img_byte_arr, format='PNG')
-        img_byte_arr.seek(0)
-        
-        files = {"file": ("image.png", img_byte_arr, "image/png")}
-        data = {"width": int(width), "height": int(height)}
-        
-        response = requests.post(f"{API_URL}/resize", files=files, data=data, timeout=10)
+        response = requests.post(f"{API_URL}/predict", files=files, timeout=120)
         response.raise_for_status()
-        result = response.json()
-        return str(result.get("resized_dimensions"))
-    except requests.exceptions.HTTPError as e:
-        return f"Error: {response.json().get('detail', str(e))}"
+        data = response.json()
+        return data.get("predicted_class")
     except Exception as e:
         return f"Error: {str(e)}"
 
-# GUI created using Gradio with Tabs
-with gr.Blocks() as iface:
-    gr.Markdown("# Image Classifier and Resizer")
-    
-    with gr.Tab("Predict"):
-        with gr.Row():
-            img_input = gr.Image(type="pil", label="Upload Image")
-        class_input = gr.Textbox(
-            value="cardboard,paper,plastic,metal,trash,glass",
-            label="Class Names (comma-separated)"
-        )
-        predict_btn = gr.Button("Predict")
-        predict_output = gr.Textbox(label="Predicted Class")
-        
-        predict_btn.click(predict_image, inputs=[img_input, class_input], outputs=predict_output)
-    
-    with gr.Tab("Resize"):
-        with gr.Row():
-            img_resize = gr.Image(type="pil", label="Upload Image")
-        with gr.Row():
-            width_input = gr.Number(value=256, label="Width")
-            height_input = gr.Number(value=256, label="Height")
-        resize_btn = gr.Button("Resize")
-        resize_output = gr.Textbox(label="New Dimensions")
-        
-        resize_btn.click(resize_image, inputs=[img_resize, width_input, height_input], outputs=resize_output)
+
+# GUI creted using Gradio
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(label="Upload Image", type="numpy", height=400),
+    outputs=gr.Textbox(label="Predicted class"),
+    title="Cat/Dog predictor GUI",
+    description="Cat/Dog predictor GUI powered by Fastapi + Render + Docker",
+)
 
 # Launch the GUI
 if __name__ == "__main__":