Update app.py

app.py

@@ -1,3 +1,144 @@
+import gradio as gr
+import torch
+from PIL import Image
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ---- CPU-only config ----
+MID = "apple/FastVLM-0.5B"
+IMAGE_TOKEN_INDEX = -200  # special image token id used by FastVLM
+
+tok = None
+model = None
+
+def load_model():
+    global tok, model
+    if tok is None or model is None:
+        print("Loading model (CPU)…")
+        tok = AutoTokenizer.from_pretrained(MID, trust_remote_code=True)
+        # Force CPU + float32 (fp16 is unsafe on CPU)
+        model = AutoModelForCausalLM.from_pretrained(
+            MID,
+            torch_dtype=torch.float32,
+            device_map="cpu",
+            trust_remote_code=True,
+        )
+        print("Model loaded successfully on CPU!")
+    return tok, model
+
+def caption_image(image, custom_prompt=None):
+    """
+    Generate a caption for the input image (CPU-only).
+    """
+    if image is None:
+        return "Please upload an image first."
+
+    try:
+        tok, model = load_model()
+
+        # Convert image to RGB if needed
+        if image.mode != "RGB":
+            image = image.convert("RGB")
+
+        prompt = custom_prompt if custom_prompt else "Describe this image in detail."
+
+        # Single-turn chat with an <image> placeholder
+        messages = [{"role": "user", "content": f"<image>\n{prompt}"}]
+        rendered = tok.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+        # Split around the literal "<image>"
+        pre, post = rendered.split("<image>", 1)
+
+        # Tokenize text around the image token
+        pre_ids = tok(pre, return_tensors="pt", add_special_tokens=False).input_ids
+        post_ids = tok(post, return_tensors="pt", add_special_tokens=False).input_ids
+
+        # Derive device/dtype from the loaded model (CPU here, but future-proof)
+        model_device = next(model.parameters()).device
+        model_dtype = next(model.parameters()).dtype
+
+        # Insert IMAGE token id at placeholder position
+        img_tok = torch.tensor([[IMAGE_TOKEN_INDEX]], dtype=pre_ids.dtype, device=model_device)
+        input_ids = torch.cat(
+            [pre_ids.to(model_device), img_tok, post_ids.to(model_device)],
+            dim=1
+        )
+        attention_mask = torch.ones_like(input_ids, device=model_device)
+
+        # Preprocess image using model's vision tower
+        px = model.get_vision_tower().image_processor(
+            images=image, return_tensors="pt"
+        )["pixel_values"].to(device=model_device, dtype=model_dtype)
+
+        # Generate caption (deterministic)
+        with torch.no_grad():
+            out = model.generate(
+                inputs=input_ids,
+                attention_mask=attention_mask,
+                images=px,
+                max_new_tokens=128,
+                do_sample=False,  # temperature is ignored when sampling is off
+            )
+
+        # Decode and slice to the assistant part if present
+        generated_text = tok.decode(out[0], skip_special_tokens=True)
+        if "Assistant:" in generated_text:
+            response = generated_text.split("Assistant:", 1)[-1].strip()
+        elif "assistant" in generated_text:
+            response = generated_text.split("assistant", 1)[-1].strip()
+        else:
+            response = generated_text.strip()
+
+        return response
+
+    except Exception as e:
+        return f"Error generating caption: {str(e)}"
+
+# ---- Gradio UI (CPU) ----
+with gr.Blocks(title="FastVLM Image Captioning (CPU)") as demo:
+    gr.Markdown(
+        """
+        # 🖼️ FastVLM Image Captioning (CPU)
+        Upload an image to generate a detailed caption using Apple's FastVLM-0.5B.
+        This build runs on **CPU only**. Expect slower generation than GPU.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Upload Image", elem_id="image-upload")
+            custom_prompt = gr.Textbox(
+                label="Custom Prompt (Optional)",
+                placeholder="Leave empty for default: 'Describe this image in detail.'",
+                lines=2
+            )
+            with gr.Row():
+                clear_btn = gr.ClearButton([image_input, custom_prompt])
+                generate_btn = gr.Button("Generate Caption", variant="primary")
+
+        with gr.Column():
+            output = gr.Textbox(
+                label="Generated Caption",
+                lines=8,
+                max_lines=15,
+                show_copy_button=True
+            )
+
+    generate_btn.click(fn=caption_image, inputs=[image_input, custom_prompt], outputs=output)
+
+    # Also generate on image upload if no custom prompt
+    def _auto_caption(img, prompt):
+        return caption_image(img, prompt) if (img is not None and not prompt) else None
+
+    image_input.change(fn=_auto_caption, inputs=[image_input, custom_prompt], outputs=output)
+
+    gr.Markdown(
+        """
+        ---
+        **Model:** [apple/FastVLM-0.5B](https://huggingface.co/apple/FastVLM-0.5B)  
+        **Note:** CPU-only run. For speed, switch to a CUDA GPU build or a GPU Space.
+        """
+    )
+
 if __name__ == "__main__":
     demo.launch(
         share=False,