Update app.py

app.py

@@ -1,106 +1,12 @@
 import gradio as gr
-import torch
-from threading import Thread
-from transformers import (
-    AutoProcessor,
-    AutoModelForImageTextToText,
-    TextIteratorStreamer,
-)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
 
-processor = AutoProcessor.from_pretrained(
-    "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
-)
+def hello(x):
+    return f"Hello {x}"
 
-model = AutoModelForImageTextToText.from_pretrained(
-    "HuggingFaceTB/SmolVLM2-2.2B-Instruct",
-    torch_dtype=torch.float16 if device == "cuda" else torch.float32,
+demo = gr.Interface(
+    fn=hello,
+    inputs=gr.Textbox(),
+    outputs=gr.Textbox(),
 )
-model.to(device)
-
-
-def infer(text, files, history, max_tokens):
-    user_content = []
-
-    if text.strip():
-        user_content.append({"type": "text", "text": text})
-
-    if files:
-        for f in files:
-            name = f.name
-            if name.lower().endswith((".png", ".jpg", ".jpeg")):
-                user_content.append({"type": "image", "path": name})
-            elif name.lower().endswith(".mp4"):
-                user_content.append({"type": "video", "path": name})
-
-    messages = [{"role": "user", "content": user_content}]
-
-    inputs = processor.apply_chat_template(
-        messages,
-        add_generation_prompt=True,
-        tokenize=True,
-        return_tensors="pt",
-    ).to(device)
-
-    streamer = TextIteratorStreamer(
-        processor, skip_prompt=True, skip_special_tokens=True
-    )
-
-    thread = Thread(
-        target=model.generate,
-        kwargs=dict(
-            inputs,
-            streamer=streamer,
-            max_new_tokens=max_tokens,
-        ),
-    )
-    thread.start()
-
-    output = ""
-    for token in streamer:
-        output += token
-        yield output
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("## 🧠 SmolVLM2 – Stable Space UI")
-
-    chatbot = gr.Chatbot(height=450)
-
-    text = gr.Textbox(
-        label="Your question",
-        placeholder="Ask something about the image or video",
-    )
-
-    files = gr.Files(
-        label="Upload image or video",
-        file_types=["image", ".mp4"],
-    )
-
-    max_tokens = gr.Slider(50, 400, value=200, step=50)
-
-    def user_submit(t, f, history):
-        history = history + [[t, None]]
-        return "", [], history
-
-    def bot_reply(history, max_tokens):
-        last_text = history[-1][0]
-        for chunk in infer(last_text, files.value, history[:-1], max_tokens):
-            history[-1][1] = chunk
-            yield history
-
-    submit = gr.Button("Send")
-
-    submit.click(
-        user_submit,
-        [text, files, chatbot],
-        [text, files, chatbot],
-        queue=False,
-    ).then(
-        bot_reply,
-        [chatbot, max_tokens],
-        chatbot,
-    )
 
-demo.launch(debug=True)
+demo.launch()