Update app.py

app.py

@@ -1 +1,110 @@
-import gradio as gr
+import pandas as pd
+from tfidf_matcher.ngrams import ngrams
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.neighbors import NearestNeighbors
+import gradio as gr
+
+def matcher(original=[], lookup=[], outname='Original', ngram_length=3, cutoff=0.8):
+    k_matches=1
+
+    # Enforce listtype, set to lower
+    original = list(original.split(","))
+    lookup = list(lookup.split(","))
+
+    # print(original)
+    # print(lookup)
+
+    original_lower = [x.lower() for x in original]
+    lookup_lower = [x.lower() for x in lookup]
+
+    # Set ngram length for TfidfVectorizer callable
+    def ngrams_user(string, n=ngram_length):
+        return ngrams(string, n)
+
+    # Generate Sparse TFIDF matrix from Lookup corpus
+    vectorizer = TfidfVectorizer(min_df=1, analyzer=ngrams_user)
+    tf_idf_lookup = vectorizer.fit_transform(lookup_lower)
+
+    # Fit KNN model to sparse TFIDF matrix generated from Lookup
+    nbrs = NearestNeighbors(n_neighbors=k_matches, n_jobs=-1, metric="cosine").fit(tf_idf_lookup)
+
+    # Use nbrs model to obtain nearest matches in lookup dataset. Vectorize first.
+    tf_idf_original = vectorizer.transform(original_lower)
+    distances, lookup_indices = nbrs.kneighbors(tf_idf_original)
+
+    # Extract top Match Score (which is just the distance to the nearest neighbour),
+    # Original match item, and Lookup matches.
+    original_name_list = []
+    confidence_list = []
+    index_list = []
+    lookup_list = []
+    print(len(lookup_indices))
+    # i is 0:len(original), j is list of lists of matches
+    for i, lookup_index in enumerate(lookup_indices):
+        original_name = original[i]
+        # lookup names in lookup list
+        lookups = [lookup[index] for index in lookup_index]
+        # transform distances to confidences and store
+        confidence = [1 - round(dist, 2) for dist in distances[i]]
+        original_name_list.append(original_name)
+        # store index
+        index_list.append(lookup_index)
+        confidence_list.append(confidence)
+        lookup_list.append(lookups)
+
+    # Convert to df
+    df_orig_name = pd.DataFrame(original_name_list, columns=[outname])
+
+    df_lookups = pd.DataFrame(
+        lookup_list, columns=["Match"]
+    )
+    df_confidence = pd.DataFrame(
+        confidence_list,
+        columns=["Match Confidence"],
+    )
+
+    # bind columns
+    matches = pd.concat([df_orig_name, df_lookups, df_confidence], axis=1)
+
+    # reorder columns | can be skipped
+    lookup_cols = list(matches.columns.values)
+    lookup_cols_reordered = [lookup_cols[0]]
+    for i in range(1, k_matches + 1):
+        lookup_cols_reordered.append(lookup_cols[i])
+        lookup_cols_reordered.append(lookup_cols[i + k_matches])
+        # lookup_cols_reordered.append(lookup_cols[i + 2 * k_matches])
+    matches = matches[lookup_cols_reordered]
+
+    matches = matches.loc[matches["Match Confidence"] > cutoff]
+    matches.sort_values(by=["Match Confidence"], ascending=False, inplace=True)
+
+    return matches
+
+def combine(a, b):
+    return a + " " + b
+
+
+with gr.Blocks() as demo:
+
+    with gr.Row():
+        with gr.Column():
+            txt = gr.Textbox(label="Input a list of names", value='Courtney Walsh,Curtly Ambrose,Malcolm Marshall,Brian Lara,Viv Richards,Obama',lines=2)
+            txt_2 = gr.Textbox(label="Input some names to match", value="Walsh, Ambrose, Marshall, Lara",lines=2)
+
+    # with gr.Row():
+        with gr.Column():
+
+            outty =  gr.Dataframe(
+                    headers=["Original", "Match", "Confidence"],
+                    datatype=["str", "str", "number"],
+                    label="Matched",
+                )
+
+
+    btn = gr.Button(value="Submit")
+    btn.click(matcher, inputs=[txt, txt_2], outputs=[outty])
+
+
+
+if __name__ == "__main__":
+    demo.launch()