Update app.py

app.py

@@ -1,43 +1,6 @@
-# import gradio as gr
-# from segmentation import segment_image
-# import numpy as np
-# import cv2
-
-# # Image de test par défaut
-# default_image_path = "./image.png"
-
-# def segment_and_display(image_path=default_image_path):
-#     # Appeler la fonction de segmentation
-#     original_image, segmented_image = segment_image(image_path)
-    
-#     # Retourner les images pour l'affichage
-#     return original_image, segmented_image
-
-# # Charger l'image de test par défaut
-# default_original_image, default_segmented_image = segment_image(default_image_path)
-
-# # Interface Gradio
-# iface = gr.Interface(
-#     fn=segment_and_display,
-#     inputs=gr.Image(type="filepath", label="Upload Image"),
-#     outputs=[
-#         gr.Image(type="numpy", label="Original Image"),
-#         gr.Image(type="numpy", label="Segmented Image")
-#     ],
-#     title="Image Segmentation with K-means (k=2)",
-#     description="Upload an image or use the default test image to see the segmentation result.",
-#     examples=[
-#         [default_image_path]
-#     ],
-#     live=True  # Permet de voir les changements en temps réel
-# )
-
-# # Afficher l'image de test par défaut lorsque l'interface est ouverte
-# iface.launch(share=True, inline=True)
 import gradio as gr
 from segmentation import segment_image
 import os
-import os
 import requests
 import base64
 
@@ -52,19 +15,26 @@ def query_medgemma(image_path, question):
     encoded_image = base64.b64encode(image_bytes).decode("utf-8")
     encoded_image = f"data:image/png;base64,{encoded_image}"
 
-
-   payload = {
-    "inputs": {
-        "image": encoded_image,
-        "question": question
+    payload = {
+        "inputs": [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": question},
+                    {"type": "image", "image": encoded_image}
+                ]
+            }
+        ]
     }
-}
 
     response = requests.post(API_URL, headers=HEADERS, json=payload)
     if response.ok:
-        return response.json()[0]["generated_text"]
+        try:
+            return response.json()[0]["generated_text"][-1]["content"]
+        except Exception as e:
+            return f"Parse Error: {str(e)}\nFull Response: {response.json()}"
     else:
-        return f"Error: {response.text}"
+        return f"Error: {response.status_code} - {response.text}"
 
 # 默认图片路径
 default_image_path = "./image.png"
@@ -86,11 +56,10 @@ with gr.Blocks() as demo:
 
         with gr.Column(scale=2):
             chatbot = gr.Textbox(label="Ask MedGemma", placeholder="Enter your medical question...")
-            image_for_analysis = gr.Image(type="filepath", label="Upload image for analysis (optional)")
+            image_for_analysis = gr.Image(type="filepath", label="Upload image for analysis")
             analyze_button = gr.Button("Analyze")
             response_output = gr.Textbox(label="Response")
 
             analyze_button.click(fn=analyze_with_medgemma, inputs=[image_for_analysis, chatbot], outputs=response_output)
 
 demo.launch()
-