Update app.py

app.py

@@ -1,59 +1,88 @@
 import gradio as gr
 from gradio_client import Client, handle_file
+from PIL import Image
 import json
 import tempfile
 
-# Initialize both external Spaces
-client1 = Client("raqiat123/crop_disease_detection")
-client2 = Client("SoraRyuu/cv_first")
+# Load both external Spaces
+resnet_client = Client("raqiat123/crop_disease_detection")
+yolo_client = Client("SoraRyuu/cv_first")
 
-def combined_predict(image):
-    # Temporarily save uploaded file
+
+def extract_best_prediction(result_dict):
+    """
+    Extracts the best label + best confidence from:
+    {
+       "label1": 0.82,
+       "label2": 0.13,
+       ...
+    }
+    """
+    if not result_dict:
+        return None, 0.0
+
+    best_label = max(result_dict, key=result_dict.get)
+    best_conf = result_dict[best_label]
+    return best_label, best_conf
+
+
+def combined_predict(image_pil):
+    """
+    Input = PIL image from Gradio
+    """
+
+    # Save the PIL image to a temp file for HF client
     with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-        image.save(tmp.name)
-        tmp_path = tmp.name
-
-    # Run prediction on both models
-    result1 = client1.predict(image=handle_file(tmp_path), api_name="/predict")
-    result2 = client2.predict(image=handle_file(tmp_path), api_name="/predict")
-
-    # Extract confidence values (adjust if needed)
-    conf1 = result1.get("confidence") if isinstance(result1, dict) else 0
-    conf2 = result2.get("confidence") if isinstance(result2, dict) else 0
-
-    if conf1 >= conf2:
-        chosen = {
-            "chosen_model": "raqiat123/crop_disease_detection",
-            "confidence": conf1,
-            "output": result1
+        image_pil.save(tmp.name)
+        img_path = tmp.name
+
+    # Run both external models
+    resnet_output = resnet_client.predict(
+        image=handle_file(img_path),
+        api_name="/predict"
+    )
+
+    # YOLO space returns (dict, image)
+    yolo_output, _ = yolo_client.predict(
+        image=handle_file(img_path),
+        api_name="/predict"
+    )
+
+    # Extract best predictions
+    resnet_label, resnet_conf = extract_best_prediction(resnet_output)
+    yolo_label, yolo_conf = extract_best_prediction(yolo_output)
+
+    # Choose best model
+    if resnet_conf >= yolo_conf:
+        final = {
+            "chosen_model": "ResNet (crop_disease_detection)",
+            "label": resnet_label,
+            "confidence": resnet_conf,
+            "full_output": resnet_output
         }
-        result_text = f"Model Selected: crop_disease_detection\nConfidence: {conf1}"
+        text = f"Model Selected: ResNet\nPrediction: {resnet_label}\nConfidence: {resnet_conf:.4f}"
     else:
-        chosen = {
-            "chosen_model": "SoraRyuu/cv_first",
-            "confidence": conf2,
-            "output": result2
+        final = {
+            "chosen_model": "YOLO (cv_first)",
+            "label": yolo_label,
+            "confidence": yolo_conf,
+            "full_output": yolo_output
         }
-        result_text = f"Model Selected: cv_first\nConfidence: {conf2}"
+        text = f"Model Selected: YOLO\nPrediction: {yolo_label}\nConfidence: {yolo_conf:.4f}"
 
-    return result_text, json.dumps(chosen, indent=4)
+    return text, final
 
 
-# Build Gradio UI
+# Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("# 🌱 Crop Classifier")
-    gr.Markdown("Upload an image. The Space will return the disease detected.")
+    gr.Markdown("# 🌿 Crop Disease Classifier")
+    gr.Markdown("Give an image to detect disease in crop, if any.")
 
-    image_input = gr.Image(type="numpy")
-    text_output = gr.Textbox(label="Result (Text)")
-    json_output = gr.JSON(label="Raw JSON Result")
+    img = gr.Image(type="pil")
+    text_output = gr.Textbox(label="Prediction")
+    json_output = gr.JSON(label="Raw JSON Output")
 
     btn = gr.Button("Run Prediction")
-
-    btn.click(
-        fn=combined_predict,
-        inputs=image_input,
-        outputs=[text_output, json_output]
-    )
+    btn.click(fn=combined_predict, inputs=img, outputs=[text_output, json_output])
 
 demo.launch()