Create app.py

app.py

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+import pandas as pd
+
+# Load the CSV file
+df = pd.read_csv("1921_catalogue_SMG.csv")  # Replace with your actual CSV file path
+text_column = "Description"  # Replace with the actual column name containing the text data
+
+# Load the model
+model = GLiNER.from_pretrained("max-long/textile_machines_3_oct", trust_remote_code=True)
+
+def get_new_snippet():
+    # Randomly select a snippet from the CSV file
+    if len(df) > 0:
+        sample = df.sample(n=1)[text_column].values[0]
+        return sample
+    else:
+        return "No more snippets available."  # Return this if the CSV file is empty
+
+def ner(text: str):
+    labels = ["Textile Machinery"]
+    threshold = 0.5
+
+    # Predict entities using the fine-tuned GLiNER model
+    entities = model.predict_entities(text, labels, flat_ner=True, threshold=threshold)
+
+    textile_entities = [
+        {
+            "entity": ent["label"],
+            "word": ent["text"],
+            "start": ent["start"],
+            "end": ent["end"],
+            "score": ent.get("score", 0),
+        }
+        for ent in entities
+        if ent["label"] == "Textile Machinery"
+    ]
+
+    highlighted_text = text
+    for ent in sorted(textile_entities, key=lambda x: x['start'], reverse=True):
+        highlighted_text = (
+            highlighted_text[:ent['start']] +
+            f"<span style='background-color: yellow; font-weight: bold;'>{highlighted_text[ent['start']:ent['end']]}</span>" +
+            highlighted_text[ent['end']:]
+        )
+
+    return highlighted_text, textile_entities
+
+# Gradio Interface
+with gr.Blocks(title="Textile Machinery NER Demo") as demo:
+    gr.Markdown(
+        """
+        # Textile Machinery Entity Recognition Demo
+        This demo selects a random text snippet from a CSV file and identifies "Textile Machinery" entities using a fine-tuned GLiNER model.
+        """
+    )
+
+    input_text = gr.Textbox(
+        value="Enter or refresh to get text from CSV",
+        label="Text input",
+        placeholder="Enter your text here",
+        lines=5
+    )
+
+    output_highlighted = gr.HTML(label="Predicted Entities")
+    output_entities = gr.JSON(label="Entities")
+
+    submit_btn = gr.Button("Find Textile Machinery!")
+    refresh_btn = gr.Button("Get New Snippet")
+
+    refresh_btn.click(fn=get_new_snippet, outputs=input_text)
+
+    submit_btn.click(
+        fn=ner,
+        inputs=[input_text],
+        outputs=[output_highlighted, output_entities]
+    )
+
+demo.queue()
+demo.launch(debug=True)