Update app.py

app.py

@@ -11,29 +11,29 @@ api_token = os.getenv("HF_TOKEN").strip()
 
 # Quantization configuration
 bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True, 
-    bnb_4bit_quant_type="nf4", 
-    bnb_4bit_use_double_quant=True, 
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
     bnb_4bit_compute_dtype=torch.float16
 )
 
 # Load model
 model = AutoModel.from_pretrained(
-    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1", 
-    quantization_config=bnb_config, 
-    device_map="auto", 
-    torch_dtype=torch.float16, 
+    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1",
+    quantization_config=bnb_config,
+    device_map="auto",
+    torch_dtype=torch.float16,
     trust_remote_code=True,
     token=api_token
 )
 
 tokenizer = AutoTokenizer.from_pretrained(
-    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1", 
+    "ContactDoctor/Bio-Medical-MultiModal-Llama-3-8B-V1",
     trust_remote_code=True,
     token=api_token
 )
 
-def analyze_input(image_data=None, question=""):
+def analyze_input(image_data, question):
     try:
         # Handle base64 image if provided
         if isinstance(image_data, str) and image_data.startswith('data:image'):
@@ -73,7 +73,7 @@ def analyze_input(image_data=None, question=""):
 demo = gr.Interface(
     fn=analyze_input,
     inputs=[
-        gr.Image(type="numpy", label="Medical Image (Optional)", optional=True),
+        gr.Image(type="numpy", label="Medical Image"),  # Removed optional parameter
         gr.Textbox(label="Question", placeholder="Enter your medical query...")
     ],
     outputs=gr.JSON(label="Analysis"),
@@ -83,4 +83,9 @@ demo = gr.Interface(
 )
 
 # Launch with API access enabled
-demo.launch(share=True, server_name="0.0.0.0", server_port=7860, enable_queue=True)
+demo.launch(
+    share=True,
+    server_name="0.0.0.0",
+    server_port=7860,
+    enable_queue=True
+)