Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Visionbuddy
-emoji: 🐨
-colorFrom: gray
-colorTo: gray
+title: visionbuddy
+app_file: app.py
 sdk: gradio
 sdk_version: 5.39.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,93 @@
+import gradio as gr
+from transformers import (
+    PaliGemmaProcessor,
+    PaliGemmaForConditionalGeneration,
+)
+import torch
+from PIL import Image
+import numpy as np
+
+# Device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print(f"Using device: {device}")
+
+# Load model and processor
+model_id = "google/paligemma2-3b-mix-448"
+model = PaliGemmaForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.float32,
+    device_map="auto",
+    low_cpu_mem_usage=True
+).eval()
+processor = PaliGemmaProcessor.from_pretrained(model_id)
+print("Model and processor loaded successfully")
+
+# Process image
+def process_image(image, task_type, question="", objects=""):
+    try:
+        if task_type == "Describe Image":
+            prompt = "describe en"
+        elif task_type == "OCR Text Recognition":
+            prompt = "ocr"
+        elif task_type == "Answer Question":
+            prompt = f"answer en {question}"
+        elif task_type == "Detect Objects":
+            prompt = f"detect {objects}"
+        else:
+            return "Please select a valid task."
+
+        if isinstance(image, np.ndarray):
+            image = Image.fromarray(image)
+
+        model_inputs = processor(text=prompt, images=image, return_tensors="pt")
+        model_inputs = {k: v.to(device) for k, v in model_inputs.items()}
+        input_len = model_inputs["input_ids"].shape[-1]
+
+        with torch.inference_mode():
+            generation = model.generate(
+                **model_inputs,
+                max_new_tokens=100,
+                do_sample=False
+            )
+            generation = generation[0][input_len:]
+            result = processor.decode(generation, skip_special_tokens=True)
+
+        return result
+    except Exception as e:
+        return f"Error during processing: {str(e)}"
+
+# Elegant website-style CSS
+custom_css = """
+
+"""
+
+# Gradio app
+with gr.Blocks(css=custom_css) as demo:
+    gr.Markdown("""<h1>PaliGemma 2 Visual AI Assistant</h1>""")
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(label="Upload Image", elem_classes="image-preview")
+            task_type = gr.Radio(
+                choices=["Describe Image", "OCR Text Recognition", "Answer Question", "Detect Objects"],
+                label="Choose Task",
+                value="Describe Image"
+            )
+            question_input = gr.Textbox(label="Question", placeholder="Type a question", visible=False)
+            objects_input = gr.Textbox(label="Objects to Detect", placeholder="e.g., cat; car", visible=False)
+            submit_btn = gr.Button("🔍 Analyze")
+
+        with gr.Column():
+            output_text = gr.Textbox(label="Result", lines=10)
+
+    def update_inputs(task):
+        return {
+            question_input: gr.update(visible=(task == "Answer Question")),
+            objects_input: gr.update(visible=(task == "Detect Objects"))
+        }
+
+    task_type.change(fn=update_inputs, inputs=[task_type], outputs=[question_input, objects_input])
+    submit_btn.click(fn=process_image, inputs=[image_input, task_type, question_input, objects_input], outputs=output_text)
+
+if __name__ == "__main__":
+    demo.launch(share=True, inbrowser=True)

requirement.txt

@@ -0,0 +1,4 @@
+gradio
+torch
+transformers
+Pillow

requirements.txt

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+gradio
+transformers
+torch
+Pillow