Upload folder using huggingface_hub

.env

@@ -0,0 +1,5 @@
+
+
+PARAM1=26f3c9103emsh4bd4451b35fd851p1b847bjsn99dfadd7f4ce
+
+PARAM2=twinword-sentiment-analysis.p.rapidapi.com

.gitattributes

@@ -32,3 +32,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+word2vec_model.tf filter=lfs diff=lfs merge=lfs -text
+~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/imdb_reviews-test.tfrecord-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/imdb_reviews-train.tfrecord-00000-of-00001 filter=lfs diff=lfs merge=lfs -text
+~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/imdb_reviews-unsupervised.tfrecord-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

.github/workflows/update_space.yml

@@ -0,0 +1,28 @@
+name: Run Python script
+
+on:
+  push:
+    branches:
+      - n
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v2
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9'
+
+    - name: Install Gradio
+      run: python -m pip install gradio
+
+    - name: Log in to Hugging Face
+      run: python -c 'import huggingface_hub; huggingface_hub.login(token="${{ secrets.hf_token }}")'
+
+    - name: Deploy to Spaces
+      run: gradio deploy

README.md

@@ -1,12 +1,7 @@
 ---
-title: Sentiment Analyzer
-emoji: 📈
-colorFrom: purple
-colorTo: blue
+title: Sentiment_Analyzer
+app_file: Sentiment_Analyzer_app.py
 sdk: gradio
 sdk_version: 3.33.1
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Sentiment_Analyzer

Sample_Sentiment_Analyzer.py

@@ -0,0 +1,50 @@
+import os
+import requests
+from textblob import TextBlob
+import matplotlib.pyplot as plt
+import os
+
+# Replace with your API endpoint and parameters
+from dotenv import load_dotenv
+
+
+# Load environment variables from .env file
+load_dotenv()
+
+headers = {
+    "X-RapidAPI-Key": os.getenv("PARAM1"),
+    "X-RapidAPI-Host": os.getenv("PARAM2")
+}
+
+
+
+# Make the API request
+url = "https://twinword-sentiment-analysis.p.rapidapi.com/analyze/"
+
+querystring = {"text":input("Enter the text to analyze:")}
+
+
+# Make the API request
+response = requests.get(url, headers=headers, params=querystring)
+
+# Check if the request was successful
+if response.status_code == 200:
+    # Extract the text data from the API response
+    text_data = response.text
+
+    # Analyze sentiment using TextBlob
+    blob = TextBlob(text_data)
+    sentiment = blob.sentiment.polarity
+
+    # Determine sentiment category
+    if sentiment > 0:
+        sentiment_category = "positive"
+    elif sentiment < 0:
+        sentiment_category = "negative"
+    else:
+        sentiment_category = "neutral"
+
+    print(f"Sentiment: {sentiment_category} ({sentiment})")
+
+else:
+    print(f"Error: API request failed with status code {response.status_code}")

Sentiment_Analyzer_app.py

@@ -0,0 +1,9 @@
+from transformers import pipeline
+classifier = pipeline("sentiment-analysis")
+
+def func(utterance):
+  return classifier(utterance)
+import gradio as gr
+descriptions = "Enter a text and get the sentiment prediction."
+app = gr.Interface(fn=func, inputs="text", outputs="text", title="Sentiment Analayzer", description=descriptions)
+app.launch(share=True)

flagged/log.csv

@@ -0,0 +1,4 @@
+Input Text,Sentiment,flag,username,timestamp
+Hello. How are you crying?,Negative,,,2023-06-05 15:02:46.499733
+Hello. How are you crying?,Negative,,,2023-06-05 15:02:49.320540
+,,,,2023-06-05 17:48:47.143025

my_model.h5

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+size 114832

my_model.tf/keras_metadata.pb

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+size 11475

my_model.tf/saved_model.pb

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+oid sha256:9f7c6cdf494ef5a22f16fb97a9791ebb56b84a5f44484840c82995f39938957b
+size 1576479

word2vec.model

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+oid sha256:973cea0662f6cf95a55fbc9a9a9fab5f49f00d0507f1ea4437657adf0edf2ff5
+size 3899332

word2vec_model.tf

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~/tensorflow_datasets/imdb_reviews/.config/metadata.json

@@ -0,0 +1 @@
+{"default_config_name": "plain_text"}

~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/dataset_info.json

@@ -0,0 +1,140 @@
+{
+  "citation": "@InProceedings{maas-EtAl:2011:ACL-HLT2011,\n  author    = {Maas, Andrew L.  and  Daly, Raymond E.  and  Pham, Peter T.  and  Huang, Dan  and  Ng, Andrew Y.  and  Potts, Christopher},\n  title     = {Learning Word Vectors for Sentiment Analysis},\n  booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},\n  month     = {June},\n  year      = {2011},\n  address   = {Portland, Oregon, USA},\n  publisher = {Association for Computational Linguistics},\n  pages     = {142--150},\n  url       = {http://www.aclweb.org/anthology/P11-1015}\n}",
+  "configDescription": "Plain text",
+  "configName": "plain_text",
+  "description": "Large Movie Review Dataset.\nThis is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.",
+  "downloadSize": "84125825",
+  "fileFormat": "tfrecord",
+  "location": {
+    "urls": [
+      "http://ai.stanford.edu/~amaas/data/sentiment/"
+    ]
+  },
+  "moduleName": "tensorflow_datasets.text.imdb",
+  "name": "imdb_reviews",
+  "releaseNotes": {
+    "1.0.0": "New split API (https://tensorflow.org/datasets/splits)"
+  },
+  "schema": {
+    "feature": [
+      {
+        "name": "label",
+        "type": "INT"
+      },
+      {
+        "name": "text",
+        "type": "BYTES"
+      }
+    ]
+  },
+  "sizeInBytes": "84125825",
+  "splits": [
+    {
+      "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}",
+      "name": "train",
+      "numBytes": "34057750",
+      "shardLengths": [
+        "25000"
+      ],
+      "statistics": {
+        "features": [
+          {
+            "name": "label",
+            "numStats": {
+              "commonStats": {
+                "numNonMissing": "25000"
+              },
+              "max": 1.0
+            }
+          },
+          {
+            "bytesStats": {
+              "commonStats": {
+                "numNonMissing": "25000"
+              }
+            },
+            "name": "text",
+            "type": "BYTES"
+          }
+        ],
+        "numExamples": "25000"
+      }
+    },
+    {
+      "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}",
+      "name": "test",
+      "numBytes": "33275595",
+      "shardLengths": [
+        "25000"
+      ],
+      "statistics": {
+        "features": [
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+            "name": "label",
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+            }
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+              }
+            },
+            "name": "text",
+            "type": "BYTES"
+          }
+        ],
+        "numExamples": "25000"
+      }
+    },
+    {
+      "filepathTemplate": "{DATASET}-{SPLIT}.{FILEFORMAT}-{SHARD_X_OF_Y}",
+      "name": "unsupervised",
+      "numBytes": "68806612",
+      "shardLengths": [
+        "50000"
+      ],
+      "statistics": {
+        "features": [
+          {
+            "name": "label",
+            "numStats": {
+              "commonStats": {
+                "numNonMissing": "50000"
+              },
+              "max": -1.0,
+              "min": -1.0
+            }
+          },
+          {
+            "bytesStats": {
+              "commonStats": {
+                "numNonMissing": "50000"
+              }
+            },
+            "name": "text",
+            "type": "BYTES"
+          }
+        ],
+        "numExamples": "50000"
+      }
+    }
+  ],
+  "supervisedKeys": {
+    "tuple": {
+      "items": [
+        {
+          "featureKey": "text"
+        },
+        {
+          "featureKey": "label"
+        }
+      ]
+    }
+  },
+  "version": "1.0.0"
+}

~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/features.json

@@ -0,0 +1,17 @@
+{
+    "pythonClassName": "tensorflow_datasets.core.features.features_dict.FeaturesDict",
+    "featuresDict": {
+        "features": {
+            "text": {
+                "pythonClassName": "tensorflow_datasets.core.features.text_feature.Text",
+                "text": {}
+            },
+            "label": {
+                "pythonClassName": "tensorflow_datasets.core.features.class_label_feature.ClassLabel",
+                "classLabel": {
+                    "numClasses": "2"
+                }
+            }
+        }
+    }
+}

~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/imdb_reviews-test.tfrecord-00000-of-00001

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+size 33675595

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~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/imdb_reviews-unsupervised.tfrecord-00000-of-00001

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~/tensorflow_datasets/imdb_reviews/plain_text/1.0.0/label.labels.txt

@@ -0,0 +1,2 @@
+neg
+pos