Access Token to HF

app.py

@@ -1,31 +1,31 @@
-import gradio as gr
-import numpy as np
-from PIL import Image
-from transformers import AutoProcessor, AutoModelForPreTraining
-import os
-
-# PaliGemma settings
-#access_token = os.getenv('HF_TOKEN')
-processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-896")
-model = AutoModelForPreTraining.from_pretrained("google/paligemma-3b-pt-896")
-
-def response_request(image,prompt):
-    inputs = processor(prompt, image, return_tensors="pt")
-    output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
-    response = processor.decode(output[0], skip_special_tokens=True)[len(prompt):]
-    return response
-
-# Interface
-iface = gr.Interface(
-    fn=response_request, 
-    inputs=[
-        gr.Image(type="pil", label="Upload Image"),
-        gr.Textbox(label="Prompt")
-    ],
-    outputs=[
-        gr.Textbox(label="Response")
-    ],
-    title="PaliGemma (google/paligemma-3b-pt-896)"
-)
-
-iface.launch()
+import gradio as gr
+import numpy as np
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForPreTraining
+import os
+
+# PaliGemma settings
+access_token = os.getenv('HF_TOKEN')
+processor = AutoProcessor.from_pretrained("google/paligemma-3b-pt-896", token=access_token)
+model = AutoModelForPreTraining.from_pretrained("google/paligemma-3b-pt-896", token=access_token)
+
+def response_request(image,prompt):
+    inputs = processor(prompt, image, return_tensors="pt")
+    output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
+    response = processor.decode(output[0], skip_special_tokens=True)[len(prompt):]
+    return response
+
+# Interface
+iface = gr.Interface(
+    fn=response_request, 
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(label="Prompt")
+    ],
+    outputs=[
+        gr.Textbox(label="Response")
+    ],
+    title="PaliGemma (google/paligemma-3b-pt-896)"
+)
+
+iface.launch()