Update app.py

app.py

@@ -15,15 +15,11 @@ tokenizer = AutoTokenizer.from_pretrained('internlm/internlm-xcomposer2d5-7b',
 model.tokenizer = tokenizer
 
 # Define the function to process input and generate a response
-def analyze_image(query, image):
-    image = Image.open(image)
-    # Convert image to required format
-    image_path = './input_image.png'
-    image.save(image_path)
-    image_list = [image_path]
-    
+def analyze_image(query, image_path):
+    image = Image.open(image_path)
+    # Convert image to required format and save temporarily if needed
     with torch.autocast(device_type='cuda', dtype=torch.float16):
-        response, _ = model.chat(tokenizer, query, image_list, do_sample=False, num_beams=3, use_meta=True)
+        response, _ = model.chat(tokenizer, query, [image_path], do_sample=False, num_beams=3, use_meta=True)
     
     return response
 
@@ -35,7 +31,7 @@ with gr.Blocks() as demo:
         query_input = gr.Textbox(label="Enter your query", placeholder="Analyze the given image in a detailed manner")
     
     with gr.Row():
-        image_input = gr.Image(label="Upload an Image", type="file")
+        image_input = gr.Image(label="Upload an Image", type="filepath")
     
     with gr.Row():
         result_output = gr.Textbox(label="Result", placeholder="Model response will appear here", interactive=False)
@@ -46,4 +42,4 @@ with gr.Blocks() as demo:
     submit_button.click(fn=analyze_image, inputs=[query_input, image_input], outputs=result_output)
 
 # Launch the Gradio interface
-demo.launch()
+demo.launch()