added lib

README.md

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+---
+title: Pinpoint Web
+emoji: 🐢
+colorFrom: red
+colorTo: pink
+sdk: gradio
+sdk_version: 3.0.20
+app_file: app.py
+pinned: false
+license: gpl-3.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import predictor
+
+def check_string(string_to_predict):
+    try:
+        is_extremist = predictor.predictor().predict(string_to_predict)
+
+
+        if is_extremist:
+            return "The message has been identified as potentially containing violent far-right extremist content."
+        else:
+            return "The message has been identified as not containing violent far-right extremist content."
+    except FileNotFoundError as e:
+        return "The message was not feature rich enough to identify, try something else. {}".format(e)
+
+demo = gr.Interface(
+    fn=check_string,
+    inputs=gr.Textbox(lines=2, placeholder="Text to predict here..."),
+    outputs="text",
+)
+
+demo.launch()

predictor.py

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+import csv
+import time
+from pprint import pprint
+import Pinpoint_Internal.FeatureExtraction
+from Pinpoint_Internal.RandomForest import *
+
+class predictor():
+
+    def __init__(self):
+        self.model = random_forest()
+        self.model.PSYCHOLOGICAL_SIGNALS_ENABLED = False  # Needs LIWC markup
+        self.model.BEHAVIOURAL_FEATURES_ENABLED = False
+        self.model.train_model(features_file=None, force_new_dataset=False,
+                          model_location=r"far-right-radical-language.model")
+        self.dict_of_users_all = {}
+        self.feature_extractor = Pinpoint_Internal.FeatureExtraction.feature_extraction(
+            violent_words_dataset_location="swears",
+            baseline_training_dataset_location="LIWC2015 Results (Storm_Front_Posts).csv")
+
+    def predict(self, string_to_predict):
+        self.__init__()
+        try:
+            os.remove("./messages.json")
+        except:
+            pass
+        try:
+            os.remove("messages.json")
+        except:
+            pass
+
+        try:
+            os.remove("./all-messages.csv")
+        except:
+            pass
+
+        users_posts = [{"username": "tmp", "timestamp": "tmp", "message": "{}".format(string_to_predict)}]
+
+        with open('all-messages.csv', 'w', encoding='utf8', newline='') as output_file:
+            writer = csv.DictWriter(output_file, fieldnames=["username", "timestamp", "message"])
+            for users_post in users_posts:
+                writer.writerow(users_post)
+
+        self.feature_extractor._get_standard_tweets("all-messages.csv")
+
+
+        with open("./messages.json", 'w') as outfile:
+            features = self.feature_extractor.completed_tweet_user_features
+
+            json.dump(features, outfile, indent=4)
+
+        rows = self.model.get_features_as_df("./messages.json", True)
+        rows.pop("is_extremist")
+
+        iter = 0
+
+        message_vector_list = []
+
+        for user_iter in range(0, len(users_posts)):
+            rows_as_json = json.loads(rows.iloc[iter].to_json())
+
+            tmp = []
+            for i in range(1, 201):
+                vect_str = "message_vector_{}".format(str(i))
+                vector = rows_as_json[vect_str]
+                tmp.append(vector)
+            message_vector_list.append(tmp)
+
+            iter = iter + 1
+
+        for row in users_posts:
+            user = row["username"]
+            timestamp = row["timestamp"]
+            message = row["message"]
+            user_unique_id = str(self.feature_extractor._get_unique_id_from_username(user))
+
+            iter = 0
+            user_found = False
+            while not user_found:
+                try:
+                    user_features = self.feature_extractor.completed_tweet_user_features[iter][user_unique_id]
+                    user_found = True
+                    break
+                except KeyError as e:
+                    iter = iter + 1
+
+            formated_vectors = [float('%.10f' % elem) for elem in user_features["message_vector"]]
+            iter = 0
+            for vector_list in message_vector_list:
+
+                if message_vector_list[iter] == formated_vectors:
+                    is_extremist = self.model.model.predict([rows.iloc[iter]])
+
+                    if is_extremist == 1:
+                        return True
+                    else:
+                        return False

requirements.txt

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+gensim
+networkx
+nltk
+numpy
+pandas
+scikit-learn
+scipy
+gradio