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.gitattributes

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README.md

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 ---
 title: Emotion Detection
-emoji: 🌍
-colorFrom: blue
-colorTo: red
-sdk: streamlit
-sdk_version: 1.28.2
+emoji: 🚀
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 3.44.1
 app_file: app.py
 pinned: false
 ---

app.py

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+import os
+import gc
+import csv
+import socket
+import json
+import huggingface_hub
+import requests
+
+import re as r
+import gradio as gr
+import pandas as pd
+
+from huggingface_hub import Repository
+from urllib.request import urlopen
+from transformers import AutoTokenizer, AutoModelWithLMHead 
+
+## connection with HF datasets
+HF_TOKEN = os.environ.get("HF_TOKEN")
+# DATASET_NAME = "emotion_detection_dataset"
+# DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/{DATASET_NAME}"
+DATASET_REPO_URL = "https://huggingface.co/datasets/pragnakalp/emotion_detection_dataset"
+DATA_FILENAME = "emotion_detection_logs.csv"
+DATA_FILE = os.path.join("emotion_detection_logs", DATA_FILENAME)
+DATASET_REPO_ID = "pragnakalp/emotion_detection_dataset"
+print("is none?", HF_TOKEN is None)
+try:
+    hf_hub_download(
+        repo_id=DATASET_REPO_ID,
+        filename=DATA_FILENAME,
+        cache_dir=DATA_DIRNAME,
+        force_filename=DATA_FILENAME
+    )
+    
+except:
+    print("file not found")
+
+repo = Repository(
+    local_dir="emotion_detection_logs", clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN
+)
+
+SENTENCES_VALUE = """Raj loves Simran.\nLast year I lost my Dog.\nI bought a new phone!\nShe is scared of cockroaches.\nWow! I was not expecting that.\nShe got mad at him."""
+## load model
+cwd = os.getcwd()
+model_path = os.path.join(cwd)
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/t5-base-finetuned-emotion")
+model_base = AutoModelWithLMHead.from_pretrained(model_path)
+
+def getIP():
+    ip_address = ''
+    try:
+    	d = str(urlopen('http://checkip.dyndns.com/')
+    			.read())
+    
+    	return r.compile(r'Address: (\d+\.\d+\.\d+\.\d+)').search(d).group(1)
+    except Exception as e:
+        print("Error while getting IP address -->",e)
+        return ip_address
+
+def get_location(ip_addr):
+    location = {}
+    try:
+        ip=ip_addr
+    
+        req_data={
+            "ip":ip,
+            "token":"pkml123"
+        }
+        url = "https://demos.pragnakalp.com/get-ip-location"
+    
+        # req_data=json.dumps(req_data)
+        # print("req_data",req_data)
+        headers = {'Content-Type': 'application/json'}
+    
+        response = requests.request("POST", url, headers=headers, data=json.dumps(req_data))
+        response = response.json()
+        print("response======>>",response)
+        return response
+    except Exception as e:
+        print("Error while getting location -->",e)
+        return location
+
+
+"""
+generate emotions of the sentences
+"""
+def get_emotion(text):
+
+    # input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
+    input_ids = tokenizer.encode(text, return_tensors='pt')
+    output = model_base.generate(input_ids=input_ids,
+                                 max_length=2)
+
+    dec = [tokenizer.decode(ids) for ids in output]
+    label = dec[0]
+    gc.collect()
+    return label
+
+def generate_emotion(article):
+    table = {'Input':[], 'Detected Emotion':[]}
+    if article.strip():
+        sen_list = article
+        sen_list = sen_list.split('\n')
+        while("" in sen_list):
+            sen_list.remove("")
+        sen_list_temp = sen_list[0:]
+        print(sen_list_temp)
+        results_dict = []
+        results = []
+    
+        for sen in sen_list_temp:
+            if(sen.strip()):
+                cur_result = get_emotion(sen)
+    
+                results.append(cur_result)
+                results_dict.append(
+                    {
+                        'sentence': sen,
+                        'emotion': cur_result
+                    }
+                )
+                
+        table = {'Input':sen_list_temp, 'Detected Emotion':results}
+        gc.collect()
+        save_data_and_sendmail(article,results_dict,sen_list, results)
+        return pd.DataFrame(table)
+    else:
+        raise gr.Error("Please enter text in inputbox!!!!")
+    
+"""
+Save generated details
+"""
+def save_data_and_sendmail(article,results_dict,sen_list,results):
+    try:
+     
+        ip_address= getIP()
+        print(ip_address)
+        location = get_location(ip_address)
+        print(location)
+        
+        add_csv = [article,results_dict,ip_address,location]
+        with open(DATA_FILE, "a") as f:
+            writer = csv.writer(f)
+            # write the data
+            writer.writerow(add_csv)
+            commit_url = repo.push_to_hub()
+            print("commit data   :",commit_url)
+    
+        url = 'https://pragnakalpdev33.pythonanywhere.com/HF_space_emotion_detection_demo'
+        # url = 'https://pragnakalpdev35.pythonanywhere.com/HF_space_emotion_detection'
+        
+        myobj = {"sentences":sen_list,"gen_results":results,"ip_addr":ip_address,'loc':location}
+        response = requests.post(url, json = myobj)
+        print("response=-----=",response.status_code)
+   
+    except Exception as e:
+        return "Error while sending mail" + str(e)
+        
+    return "Successfully save data"
+    
+"""
+UI design for demo using gradio app
+"""
+inputs = gr.Textbox(value=SENTENCES_VALUE,lines=3, label="Sentences",elem_id="inp_div")
+outputs = [gr.Dataframe(row_count = (3, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Detected Emotion"],wrap=True)]
+
+demo = gr.Interface(
+    generate_emotion,
+    inputs,
+    outputs,
+    title="Emotion Detection",
+    css=".gradio-container {background-color: lightgray} #inp_div {background-color: #FB3D5;}",
+    article="""<p style='text-align: center;'>Provide us your <a href="https://www.pragnakalp.com/contact/" target="_blank">feedback</a> on this demo and feel free 
+            to contact us at <a href="mailto:letstalk@pragnakalp.com" target="_blank">letstalk@pragnakalp.com</a> if you want to have your own Emotion Detection system. 
+            We will be happy to serve you for your requirement. And don't forget to check out more interesting 
+            <a href="https://www.pragnakalp.com/services/natural-language-processing-services/" target="_blank">NLP services</a> we are offering.</p>
+            <p style='text-align: center;'>Developed by: <a href="https://www.pragnakalp.com" target="_blank">Pragnakalp Techlabs</a></p>"""
+)
+demo.launch()

config.json

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+{
+  "architectures": [
+    "T5ForConditionalGeneration"
+  ],
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "n_positions": 512,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "task_specific_params": {
+    "summarization": {
+      "early_stopping": true,
+      "length_penalty": 2.0,
+      "max_length": 200,
+      "min_length": 30,
+      "no_repeat_ngram_size": 3,
+      "num_beams": 4,
+      "prefix": "summarize: "
+    },
+    "translation_en_to_de": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to German: "
+    },
+    "translation_en_to_fr": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to French: "
+    },
+    "translation_en_to_ro": {
+      "early_stopping": true,
+      "max_length": 300,
+      "num_beams": 4,
+      "prefix": "translate English to Romanian: "
+    }
+  },
+  "vocab_size": 32128
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:707b2f7cae8cd8befb88d71f9b161e9297175f685c2c7bf91e151322d346a045
+size 891692894

requirements.txt

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+transformers==4.30.2
+sentencepiece==0.1.99
+torch==2.0.1
+gradio== 3.44.1

special_tokens_map.json

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+{"eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>", "additional_special_tokens": ["<extra_id_0>", "<extra_id_1>", "<extra_id_2>", "<extra_id_3>", "<extra_id_4>", "<extra_id_5>", "<extra_id_6>", "<extra_id_7>", "<extra_id_8>", "<extra_id_9>", "<extra_id_10>", "<extra_id_11>", "<extra_id_12>", "<extra_id_13>", "<extra_id_14>", "<extra_id_15>", "<extra_id_16>", "<extra_id_17>", "<extra_id_18>", "<extra_id_19>", "<extra_id_20>", "<extra_id_21>", "<extra_id_22>", "<extra_id_23>", "<extra_id_24>", "<extra_id_25>", "<extra_id_26>", "<extra_id_27>", "<extra_id_28>", "<extra_id_29>", "<extra_id_30>", "<extra_id_31>", "<extra_id_32>", "<extra_id_33>", "<extra_id_34>", "<extra_id_35>", "<extra_id_36>", "<extra_id_37>", "<extra_id_38>", "<extra_id_39>", "<extra_id_40>", "<extra_id_41>", "<extra_id_42>", "<extra_id_43>", "<extra_id_44>", "<extra_id_45>", "<extra_id_46>", "<extra_id_47>", "<extra_id_48>", "<extra_id_49>", "<extra_id_50>", "<extra_id_51>", "<extra_id_52>", "<extra_id_53>", "<extra_id_54>", "<extra_id_55>", "<extra_id_56>", "<extra_id_57>", "<extra_id_58>", "<extra_id_59>", "<extra_id_60>", "<extra_id_61>", "<extra_id_62>", "<extra_id_63>", "<extra_id_64>", "<extra_id_65>", "<extra_id_66>", "<extra_id_67>", "<extra_id_68>", "<extra_id_69>", "<extra_id_70>", "<extra_id_71>", "<extra_id_72>", "<extra_id_73>", "<extra_id_74>", "<extra_id_75>", "<extra_id_76>", "<extra_id_77>", "<extra_id_78>", "<extra_id_79>", "<extra_id_80>", "<extra_id_81>", "<extra_id_82>", "<extra_id_83>", "<extra_id_84>", "<extra_id_85>", "<extra_id_86>", "<extra_id_87>", "<extra_id_88>", "<extra_id_89>", "<extra_id_90>", "<extra_id_91>", "<extra_id_92>", "<extra_id_93>", "<extra_id_94>", "<extra_id_95>", "<extra_id_96>", "<extra_id_97>", "<extra_id_98>", "<extra_id_99>"]}

tokenizer_config.json

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+{"model_max_length": 512}