Upload run.py

run.py

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+from components.vector_db_operations import get_collection_from_vector_db
+from components.vector_db_operations import retrieval
+from components.english_information_extraction import english_information_extraction
+from components.multi_lingual_model import MDFEND , loading_model_and_tokenizer
+from components.data_loading import preparing_data , loading_data
+from components.language_identification import language_identification
+
+
+
+def run_pipeline(input_text:str):
+
+    language_dict = language_identification(input_text)
+    language_code = next(iter(language_dict))
+
+    if language_code == "en":
+
+      output_english = english_information_extraction(input_text)
+
+      return output_english
+
+    else:
+
+
+        num_results = 1
+        path = "/content/drive/MyDrive/general_domains/vector_database"
+        collection_name = "general_domains"
+
+
+        collection = get_collection_from_vector_db(path , collection_name)
+
+        domain , label_domain , distance  = retrieval(input_text  , num_results , collection )
+
+        if distance >1.45:
+          domain = "undetermined"
+
+        tokenizer , model = loading_model_and_tokenizer()
+
+        df = preparing_data(input_text , label_domain)
+
+        input_ids , input_masks , input_domains = loading_data(tokenizer , df )
+
+        labels = []
+        outputs = []
+        with torch.no_grad():
+
+            pred = model.forward(input_ids, input_masks , input_domains)
+            labels.append([])
+
+            for output in pred:
+                number = output.item()
+                label = int(1) if number >= 0.5 else int(0)
+                labels[-1].append(label)
+            outputs.append(pred)
+
+        discrimination_class = ["discriminative" if i == int(1) else "not discriminative" for i in labels[0]]
+
+
+        return { "domain_label" :domain ,
+                "domain_score":distance ,
+                 "discrimination_label" : discrimination_class[-1],
+                 "discrimination_score" : outputs[0][1:].item(),
+        }
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