Create app.py

app.py

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+import gradio as gr
+import requests
+
+# Hugging Face API details
+HF_API_TOKEN = "your_huggingface_api_token"  # Replace with your API key
+MODEL_NAME = "mistralai/Mistral-7B-Instruct"  # Choose an LLM model
+
+
+# Function to query the LLM
+def extract_info(sentence):
+    prompt = f"""
+    Extract the following information from the given sentence:
+    - Action (normalized to "add" or "remove")
+    - Item name(s)
+    - Quantity (exact number, "some", "all", or null if unspecified)
+    - Location (null if not mentioned)
+    Sentence: "{sentence}"
+    Provide the response in structured JSON format.
+    """
+
+    response = requests.post(
+        f"https://api-inference.huggingface.co/models/{MODEL_NAME}",
+        headers={"Authorization": f"Bearer {HF_API_TOKEN}"},
+        json={"inputs": prompt}
+    )
+    return response.json()
+
+# Gradio UI
+iface = gr.Interface(
+    fn=extract_info,
+    inputs=gr.Textbox(label="Enter a sentence"),
+    outputs=gr.JSON(label="Extracted Information"),
+    title="Action & Item Extractor",
+    description="Enter a sentence, and the model will extract the action, item name, quantity, and location."
+)
+
+iface.launch()