changing endpoint call

main.py

@@ -86,28 +86,26 @@ async def upload_and_encode_image(file: UploadFile = File(...)):
         raise HTTPException(status_code=400, detail=f"Invalid file: {e}")
 
 @app.get("/predict")
-def predict(image_url: str = Query(...), prompt: str = Query(...)):
+def predict(data: str = Query(...), prompt: str = Query(...)):
     """
     Generates a description for an image using the Qwen-2-VL model.
 
     Args:
-        image_url (str): The URL of the image to describe.
+        data (any): The encoded image and the prompt to be used.
         prompt (str): The text prompt to guide the model's response.
 
     Returns:
         str: The generated description of the image.
     """
 
-    image = encode_image(image_url)
-
 
     # Create the input message structure
     messages = [
         {
             "role": "user",
             "content": [
-                {"type": "image", "image": f"data:image;base64,{image}"},
-                {"type": "text", "text": prompt},
+                {"type": "image", "image": f"data:image;base64,{data.image_base64}"},
+                {"type": "text", "text": data.prompt},
             ],
         }
     ]

model.py

@@ -1,22 +1,37 @@
 import requests
 
-# curl -G "https://<uname>-<spacename>.hf.space/predict" \
-#      --data-urlencode "image_url=https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg" \
-#      --data-urlencode "prompt=Describe this image."
+# Define base URL for your Hugging Face Space
+BASE_URL = "https://danilohssantana-qwen2-5-vl-api.hf.space"
 
+# Image URL to be encoded
+image_url = "https://cdn.britannica.com/35/238335-050-2CB2EB8A/Lionel-Messi-Argentina-Netherlands-World-Cup-Qatar-2022.jpg"
 
-url = "https://danilohssantana-qwen2-5-vl-api.hf.space/predict"
+# Step 1: Download the image
+response = requests.get(image_url)
+if response.status_code != 200:
+    print("Error downloading image:", response.status_code, response.text)
+    exit()
 
-# Define the parameters
-params = {
-    "image_url": "https://cdn.britannica.com/35/238335-050-2CB2EB8A/Lionel-Messi-Argentina-Netherlands-World-Cup-Qatar-2022.jpg",
-    "prompt": "describe",
+# Step 2: Send the image to the encode-image endpoint
+files = {"file": ("image.jpg", response.content, "image/jpeg")}
+encode_response = requests.post(f"{BASE_URL}/encode-image/", files=files)
+
+if encode_response.status_code != 200:
+    print("Error encoding image:", encode_response.status_code, encode_response.text)
+    exit()
+
+encoded_image = encode_response.json().get("encoded_image")
+
+# Step 3: Send the encoded image to the predict endpoint
+predict_payload = {
+    "image_base64": encoded_image,
+    "prompt": "describe the image",
 }
 
-# Send the GET request
-response = requests.get(url, params=params)
+predict_response = requests.get(f"{BASE_URL}/predict", params=predict_payload)
 
-if response.status_code == 200:
-    print("Response:", response.json())
+# Step 4: Print the response
+if predict_response.status_code == 200:
+    print("Response:", predict_response.json())
 else:
-    print("Error:", response.status_code, response.text)
+    print("Error:", predict_response.status_code, predict_response.text)