Update app.py

app.py

@@ -1,64 +1,47 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+import torch
+from PIL import Image
+from huggingface_hub import hf_hub_download
+import sys
+import os
+
+# Ensure our working directory has the nanoVLM code
+REPO_ID = "huggingface/nanoVLM"
+LOCAL_MODEL_DIR = "models"
+if not os.path.isdir(LOCAL_MODEL_DIR):
+    # clone just the models folder
+    from git import Repo
+    Repo.clone_from("https://github.com/huggingface/nanoVLM.git", ".", depth=1, no_single_branch=True, multi_options=["--filter=blob:none","--sparse"])
+    # enable sparse checkout of models/
+    Repo().git.sparse_checkout("set", "models")
+
+# Add to path so we can import
+sys.path.insert(0, os.path.abspath(LOCAL_MODEL_DIR))
+
+from vision_language_model import VisionLanguageModel
+
+# Load the VLM
+model = VisionLanguageModel.from_pretrained("lusxvr/nanoVLM-222M")
+model.eval()
+
+def predict(img: Image.Image, prompt: str = "") -> str:
+    # Preprocess image, add batch dimension
+    img_tensor = model.preprocess_image(img).unsqueeze(0)  # (1, 3, H, W)
+    with torch.no_grad():
+        # generate_text handles your prompt internally
+        output = model.generate_text(img_tensor, prompt=prompt)
+    return output
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(lines=1, placeholder="Prompt (e.g. 'What is in this picture?')", label="Prompt")
     ],
+    outputs=gr.Textbox(label="Model Output"),
+    title="nanoVLM-222M Vision-Language Demo",
+    description="A minimal Gradio app for image captioning and VQA with nanoVLM-222M."
 )
 
-
 if __name__ == "__main__":
     demo.launch()