Task-313 Fix regression value output and reformat UI to look better

Pipfile

@@ -12,6 +12,9 @@ transformers = "*"
 torch = "*"
 huggingface-hub = "*"
 joblib = "*"
+nltk = "*"
+htbuilder = "*"
+nest-asyncio = "*"
 
 [dev-packages]
 

Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "c52664113fb789224f8338560a034a86739fe4d813ded69beb069fdc571c1fd4"
+            "sha256": "675e02cbb23625b60f9b1ab6ac0ae5b84786b0def940f4f98e8000207075ba67"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -26,11 +26,11 @@
         },
         "attrs": {
             "hashes": [
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-            "version": "==25.1.0"
+            "version": "==25.3.0"
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         "blinker": {
             "hashes": [
@@ -172,19 +172,19 @@
         },
         "filelock": {
             "hashes": [
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             "markers": "python_version >= '3.9'",
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+            "version": "==3.18.0"
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         "fsspec": {
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-            "version": "==2025.2.0"
+            "version": "==2025.3.0"
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@@ -206,17 +206,18 @@
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+            "index": "pypi",
             "markers": "python_version >= '3.7'",
             "version": "==0.9.0"
         },
         "huggingface-hub": {
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-            "version": "==0.29.2"
+            "version": "==0.29.3"
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@@ -335,11 +336,20 @@
         },
         "narwhals": {
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-            "version": "==1.29.0"
+            "version": "==1.32.0"
+        },
+        "nest-asyncio": {
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+            "version": "==1.6.0"
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         "networkx": {
             "hashes": [
@@ -349,67 +359,76 @@
             "markers": "python_version >= '3.10'",
             "version": "==3.4.2"
         },
+        "nltk": {
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@@ -547,20 +566,20 @@
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+                "sha256:1832f0515b62d12d8e6ffc078d7e9eb06969aa6dc13c13e1036e39d73bebc2de",
+                "sha256:307ecba1d852ec237e9ba668e087326a67564ef83e45a0189a772ede9e854dd0",
+                "sha256:3fde11b505e1597f71b875ef2fc52062b6a9740e5f7c8997ce878b6009145862",
+                "sha256:476cb7b14914c780605a8cf62e38c2a85f8caff2e28a6a0bad827ec7d6c85d68",
+                "sha256:4f1dfcd7997b31ef8f53ec82781ff434a28bf71d9102ddde14d076adcfc78c99",
+                "sha256:678974e1e3a9b975b8bc2447fca458db5f93a2fb6b0c8db46b6675b5b5346812",
+                "sha256:aec4962f9ea93c431d5714ed1be1c93f13e1a8618e70035ba2b0564d9e633f2e",
+                "sha256:bcefcdf3976233f8a502d265eb65ea740c989bacc6c30a58290ed0e519eb4b8d",
+                "sha256:d7d3f7d1d5a66ed4942d4fefb12ac4b14a29028b209d4bfb25c68ae172059922",
+                "sha256:fd32223020cb25a2cc100366f1dedc904e2d71d9322403224cdde5fdced0dabe"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==5.29.3"
+            "version": "==5.29.4"
         },
         "pyarrow": {
             "hashes": [
@@ -628,10 +647,10 @@
         },
         "pytz": {
             "hashes": [
-                "sha256:89dd22dca55b46eac6eda23b2d72721bf1bdfef212645d81513ef5d03038de57",
-                "sha256:c2db42be2a2518b28e65f9207c4d05e6ff547d1efa4086469ef855e4ab70178e"
+                "sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3",
+                "sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00"
             ],
-            "version": "==2025.1"
+            "version": "==2025.2"
         },
         "pyyaml": {
             "hashes": [
@@ -848,11 +867,11 @@
         },
         "setuptools": {
             "hashes": [
-                "sha256:4880473a969e5f23f2a2be3646b2dfd84af9028716d398e46192f84bc36900d2",
-                "sha256:558e47c15f1811c1fa7adbd0096669bf76c1d3f433f58324df69f3f5ecac4e8f"
+                "sha256:18fd474d4a82a5f83dac888df697af65afa82dec7323d09c3e37d1f14288da54",
+                "sha256:3e386e96793c8702ae83d17b853fb93d3e09ef82ec62722e61da5cd22376dcd8"
             ],
             "markers": "python_version >= '3.12'",
-            "version": "==75.8.2"
+            "version": "==78.1.0"
         },
         "six": {
             "hashes": [
@@ -881,12 +900,12 @@
         },
         "streamlit": {
             "hashes": [
-                "sha256:c10c09f9d1251fa7f975dd360572f03cabc82b174f080e323bf7e556103c22e0",
-                "sha256:cf94b1e9f1de75e4e383df53745230feaac4ac7a7e1f14a3ea362df134db8510"
+                "sha256:98510d03e53622bba8f0e9f2fd4f1191b3b55e5c7e55abbbaa0289cb9e21cdea",
+                "sha256:da75933bae94595167f43822dea43fcdde0d747433f7d04989266d78967951bb"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.9' and python_full_version != '3.9.7'",
-            "version": "==1.43.0"
+            "version": "==1.44.0"
         },
         "sympy": {
             "hashes": [
@@ -906,24 +925,24 @@
         },
         "tokenizers": {
             "hashes": [
-                "sha256:089d56db6782a73a27fd8abf3ba21779f5b85d4a9f35e3b493c7bbcbbf0d539b",
-                "sha256:3c4c93eae637e7d2aaae3d376f06085164e1660f89304c0ab2b1d08a406636b2",
-                "sha256:400832c0904f77ce87c40f1a8a27493071282f785724ae62144324f171377273",
-                "sha256:4145505a973116f91bc3ac45988a92e618a6f83eb458f49ea0790df94ee243ff",
-                "sha256:6b177fb54c4702ef611de0c069d9169f0004233890e0c4c5bd5508ae05abf193",
-                "sha256:6b43779a269f4629bebb114e19c3fca0223296ae9fea8bb9a7a6c6fb0657ff8e",
-                "sha256:87841da5a25a3a5f70c102de371db120f41873b854ba65e52bccd57df5a3780c",
-                "sha256:9aeb255802be90acfd363626753fda0064a8df06031012fe7d52fd9a905eb00e",
-                "sha256:c87ca3dc48b9b1222d984b6b7490355a6fdb411a2d810f6f05977258400ddb74",
-                "sha256:d8b09dbeb7a8d73ee204a70f94fc06ea0f17dcf0844f16102b9f414f0b7463ba",
-                "sha256:e84ca973b3a96894d1707e189c14a774b701596d579ffc7e69debfc036a61a04",
-                "sha256:eb1702c2f27d25d9dd5b389cc1f2f51813e99f8ca30d9e25348db6585a97e24a",
-                "sha256:eb7202d231b273c34ec67767378cd04c767e967fda12d4a9e36208a34e2f137e",
-                "sha256:ee0894bf311b75b0c03079f33859ae4b2334d675d4e93f5a4132e1eae2834fe4",
-                "sha256:f53ea537c925422a2e0e92a24cce96f6bc5046bbef24a1652a5edc8ba975f62e"
+                "sha256:0f0dcbcc9f6e13e675a66d7a5f2f225a736745ce484c1a4e07476a89ccdad382",
+                "sha256:1039a3a5734944e09de1d48761ade94e00d0fa760c0e0551151d4dd851ba63e3",
+                "sha256:28da6b72d4fb14ee200a1bd386ff74ade8992d7f725f2bde2c495a9a98cf4d9f",
+                "sha256:2dd9a0061e403546f7377df940e866c3e678d7d4e9643d0461ea442b4f89e61a",
+                "sha256:2fdbd4c067c60a0ac7eca14b6bd18a5bebace54eb757c706b47ea93204f7a37c",
+                "sha256:34d8cfde551c9916cb92014e040806122295a6800914bab5865deb85623931cf",
+                "sha256:9ac78b12e541d4ce67b4dfd970e44c060a2147b9b2a21f509566d556a509c67d",
+                "sha256:a1bb04dc5b448985f86ecd4b05407f5a8d97cb2c0532199b2a302a604a0165ab",
+                "sha256:a21a15d5c8e603331b8a59548bbe113564136dc0f5ad8306dd5033459a226da0",
+                "sha256:aaa852d23e125b73d283c98f007e06d4595732104b65402f46e8ef24b588d9f8",
+                "sha256:cd51cd0a91ecc801633829fcd1fda9cf8682ed3477c6243b9a095539de4aecf3",
+                "sha256:db9484aeb2e200c43b915a1a0150ea885e35f357a5a8fabf7373af333dcc8dbf",
+                "sha256:e5a69c1a4496b81a5ee5d2c1f3f7fbdf95e90a0196101b0ee89ed9956b8a168f",
+                "sha256:e78e413e9e668ad790a29456e677d9d3aa50a9ad311a40905d6861ba7692cf41",
+                "sha256:ed248ab5279e601a30a4d67bdb897ecbe955a50f1e7bb62bd99f07dd11c2f5b6"
             ],
-            "markers": "python_version >= '3.7'",
-            "version": "==0.21.0"
+            "markers": "python_version >= '3.9'",
+            "version": "==0.21.1"
         },
         "toml": {
             "hashes": [
@@ -987,28 +1006,28 @@
         },
         "transformers": {
             "hashes": [
-                "sha256:6b4fded1c5fee04d384b1014495b4235a2b53c87503d7d592423c06128cbbe03",
-                "sha256:7e40e640b5b8dc3f48743f5f5adbdce3660c82baafbd3afdfc04143cdbd2089e"
+                "sha256:6ee542d2cce7e1b6a06ae350599c27ddf2e6e45ec9d0cb42915b37fca3d6399a",
+                "sha256:e9b9bd274518150528c1d745c7ebba72d27e4e52f2deffaa1fddebad6912da5d"
             ],
             "index": "pypi",
             "markers": "python_full_version >= '3.9.0'",
-            "version": "==4.49.0"
+            "version": "==4.50.1"
         },
         "typing-extensions": {
             "hashes": [
-                "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d",
-                "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"
+                "sha256:0a4ac55a5820789d87e297727d229866c9650f6521b64206413c4fbada24d95b",
+                "sha256:c8dd92cc0d6425a97c18fbb9d1954e5ff92c1ca881a309c45f06ebc0b79058e5"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==4.12.2"
+            "version": "==4.13.0"
         },
         "tzdata": {
             "hashes": [
-                "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694",
-                "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"
+                "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8",
+                "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"
             ],
             "markers": "python_version >= '2'",
-            "version": "==2025.1"
+            "version": "==2025.2"
         },
         "urllib3": {
             "hashes": [

config.py

@@ -15,8 +15,9 @@ BIN_MODEL_PATH = MODELS_DIR / 'binary_classification'
 ML_MODEL_PATH = MODELS_DIR / 'multilabel_regression'
 
 # HF Hub Repositories
-BIN_REPO = 'dlsmallw/Binary-Classification-testing'
-ML_REPO = 'dlsmallw/Multilabel-Regression-testing'
+BIN_REPO = 'dlsmallw/NLPinitiative-Binary-Classification'
+ML_REPO = 'dlsmallw/NLPinitiative-Multilabel-Regression'
+DATASET_REPO = 'dlsmallw/NLPinitiative-Dataset'
 
 BIN_API_URL = f"https://api-inference.huggingface.co/models/{BIN_REPO}"
 ML_API_URL = f"https://api-inference.huggingface.co/models/{ML_REPO}"

scripts/predict.py

@@ -4,6 +4,8 @@ Script file used for performing inference with an existing model.
 
 import torch
 import json
+import nltk
+from nltk.tokenize import sent_tokenize
 import huggingface_hub
 
 from transformers import (
@@ -20,6 +22,7 @@ class InferenceHandler:
         self.api_token = api_token
         self.bin_tokenizer, self.bin_model = self.init_model_and_tokenizer(BIN_REPO)
         self.ml_regr_tokenizer, self.ml_regr_model = self.init_model_and_tokenizer(ML_REPO)
+        nltk.download('punkt_tab')
 
     def get_config(self, repo_id):
         config = None
@@ -57,32 +60,45 @@ class InferenceHandler:
         return bin_inputs, ml_inputs
     
     ## Handles performing the full sentiment analysis (binary classification and multilabel regression)
-    def classify_text(self, text):
-        res_obj = {
-            'raw_text': text,
-            'text_sentiment': None,
-            'numerical_sentiment': None,
-            'category_sentiments': {
-                'Gender': None,
-                'Race': None,
-                'Sexuality': None,  
-                'Disability': None,
-                'Religion': None,  
-                'Unspecified': None
-            }
+    def classify_text(self, input):
+        result = {
+            'text_input': input,
+            'results': []
         }
 
-        text_prediction, pred_class = self.discriminatory_inference(text)
-        res_obj['text_sentiment'] = text_prediction
-        res_obj['numerical_sentiment'] = pred_class
-
-        if pred_class == 1:
-            ml_infer_results = self.category_inference(text)
-
-            for idx, key in enumerate(res_obj['category_sentiments'].keys()):
-                res_obj['category_sentiments'][key] = ml_infer_results[idx]
+        sent_res_arr = []
+        sentences = sent_tokenize(input)
+        for sent in sentences:
+            text_prediction, pred_class = self.discriminatory_inference(sent)
+
+            sent_result = {
+                'sentence': sent,
+                'binary_classification': {
+                    'classification': text_prediction,
+                    'prediction_class': pred_class
+                },
+                'multilabel_regression': None
+            }
 
-        return res_obj
+            if pred_class == 1:
+                ml_results = {
+                    "Gender": None,
+                    "Race": None,
+                    "Sexuality": None,
+                    "Disability": None,
+                    "Religion": None,
+                    "Unspecified": None
+                }
+
+                ml_infer_results = self.category_inference(sent)
+                for idx, key in enumerate(ml_results.keys()):
+                    ml_results[key] = min(max(ml_infer_results[idx], 0.0), 1.0)
+
+                sent_result['multilabel_regression'] = ml_results
+            sent_res_arr.append(sent_result)
+
+        result['results'] = sent_res_arr
+        return result
     
     ## Handles logic for checking the binary classfication of the text 
     def discriminatory_inference(self, text):

setup.sh

@@ -0,0 +1,34 @@
+#!/usr/bin/bash
+
+build() {
+    clean 
+
+    python -m venv .venv
+    source .venv/Scripts/activate
+    pip install pipenv
+    pipenv install
+}
+
+clean() {
+    if [ -n "$VIRTUAL_ENV" ]; then
+        deactivate
+        find . -type d -name ".venv" -delete
+    fi
+
+    find . -type f -name "*.py[co]" -delete
+	find . -type d -name "__pycache__" -delete
+}
+
+# docs() {
+#     case $1 in
+#         build)
+#             mkdocs build
+#             ;;
+#         serve)
+#             mkdocs serve
+#             ;;
+#         *)
+#             log_error "Specify 'build' or 'serve'. For example: docs build"
+#             ;;
+#     esac
+# }