Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from datasets import load_dataset
+
+# Load and parse the CSV file from Hugging Face
+def load_data():
+    dataset = load_dataset("your-username/your-dataset-name", split="train")
+    df = pd.DataFrame(dataset)
+    lemmas = {}
+    current_lemma = None
+    
+    for _, row in df.iterrows():
+        if row['#ORTO'] == '---':
+            current_lemma = None
+        elif current_lemma is None:
+            current_lemma = row['#ORTO'].replace("ORTO:", "")
+            lemmas[current_lemma] = []
+        else:
+            lemma_data = {
+                'PPOS': row['#PPOS'].replace("PPOS:", "") if pd.notna(row['#PPOS']) else "",
+                'PHON1': row['#PHON1'].replace("PHON:", "") if pd.notna(row['#PHON1']) else "",
+                'PHON2': row['#PHON2'].replace("PHON:", "") if pd.notna(row['#PHON2']) else "",
+                'COMM': row['#COMM'] if pd.notna(row['#COMM']) else ""
+            }
+            lemmas[current_lemma].append(lemma_data)
+    
+    return lemmas
+
+lemmas = load_data()
+
+def search_lemma(lemma):
+    results = lemmas.get(lemma, None)
+    if not results:
+        return f"No results found for {lemma}"
+    response = f"Results for {lemma}:\n\n"
+    response += "PPOS\tPHON1\tPHON2\tCOMM\n"
+    for result in results:
+        response += f"{result['PPOS']}\t{result['PHON1']}\t{result['PHON2']}\t{result['COMM']}\n"
+    return response
+
+iface = gr.Interface(
+    fn=search_lemma,
+    inputs="text",
+    outputs="text",
+    title="Lemma Search",
+    description="Enter a lemma to search for its declensions and pronunciations."
+)
+
+if __name__ == "__main__":
+    iface.launch()