Create app.py

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Загрузка модели
+model_id = "LiquidAI/LFM2-350M-Extract"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+system_prompt = """Identify and extract information matching the following schema.
+Return data as a JSON object. Missing data should be omitted.
+Schema:
+- product: "Product name"
+- price: "Price in dollars"
+- quantity: "Number of items"
+"""
+
+def extract_info(user_input):
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_input}
+    ]
+    inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to(model.device)
+    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0, do_sample=False)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Создаем интерфейс Gradio
+demo = gr.Interface(
+    fn=extract_info,
+    inputs=gr.Textbox(label="Input Text"),
+    outputs=gr.Textbox(label="Extracted JSON")
+)
+
+demo.launch()