Update app.py

app.py

@@ -1,12 +1,17 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelWithLMHead 
 import gc
 import os
 import csv
-import pandas as pd
+import socket
 import huggingface_hub
+
+import gradio as gr
+import pandas as pd
+
 from huggingface_hub import Repository
+from transformers import AutoTokenizer, AutoModelWithLMHead 
+
 
+## connection with HF datasets
 HF_TOKEN = os.environ.get("HF_TOKEN")
 DATASET_NAME = "emotion_detection"
 DATASET_REPO_URL = f"https://huggingface.co/datasets/pragnakalp/{DATASET_NAME}"
@@ -14,13 +19,6 @@ DATA_FILENAME = "emotion_detection_logs.csv"
 DATA_FILE = os.path.join("emotion_detection_logs", DATA_FILENAME)
 DATASET_REPO_ID = "pragnakalp/emotion_detection"
 print("is none?", HF_TOKEN is None)
-
-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."""
-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)
-
 try:
     hf_hub_download(
         repo_id=DATASET_REPO_ID,
@@ -36,6 +34,45 @@ 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)
+
+"""
+get ip address
+"""
+def get_device_ip_address():
+    result = {}
+    if os.name == "nt":
+        result = "Running on Windows"
+        hostname = socket.gethostname()
+        ip_address = socket.gethostbyname(hostname)
+        result['ip_addr'] = ip_address
+        result['host'] = hostname
+        print(result)
+        return result
+    elif os.name == "posix":
+        gw = os.popen("ip -4 route show default").read().split()
+        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+        s.connect((gw[2], 0))
+        ipaddr = s.getsockname()[0]
+        gateway = gw[2]
+        host = socket.gethostname()
+        result['ip_addr'] = ipaddr
+        result['host'] = host
+        print(result)
+        return result
+    else:
+        result['id'] = os.name + " not supported yet."
+        print(result)
+        return result
+
+"""
+generate emotions of the sentences
+"""
 def get_emotion(text):
 
     # input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
@@ -67,55 +104,42 @@ def generate_emotion(article):
                     'emotion': cur_result
                 }
             )
-
-    # result = {
-    #     'result': results_dict,
-    # }
+            
     result = {'Input':sen_list_temp, 'Detected Emotion':results}
     gc.collect()
-    add_csv = [results_dict]
-    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)
-
+    save_data_and_sendmail(results_dict,sen_list, results)
     return pd.DataFrame(result)
+    
 """
 Save generated details
 """
-# def save_data_and_sendmail(article,generated_questions,num_que,result):
-#     try:
-#         hostname = {}
-#         hostname = get_device_ip_address()
-#         url = 'https://pragnakalpdev35.pythonanywhere.com/HF_space_que_gen'
-#         # url = 'http://pragnakalpdev33.pythonanywhere.com/HF_space_question_generator'
-#         myobj = {'article': article,'total_que': num_que,'gen_que':result,'ip_addr':hostname.get("ip_addr",""),'host':hostname.get("host","")}
-#         x = requests.post(url, json = myobj) 
-#         add_csv = [article, generated_questions, num_que]
-#         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)
-#     # except Exception as e:
-#     #     return "Error while storing data -->" + e
+def save_data_and_sendmail(results_dict,sen_list,results):
+    try:
+        hostname = {}
         
-#     # try:  
-#         # with open(DATA_FILE, "r") as file:
-#         #     data = json.load(file)
-#         # data.append(entry)
-#         # with open(DATA_FILE, "w") as file:
-#         #     json.dump(data, file)
-#         #     commit_url = repo.push_to_hub()
-#     except Exception as e:
-#         return "Error while sending mail" + e
+        add_csv = [results_dict]
+        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)
         
-#     return "Successfully save data"
-
-inputs = gr.Textbox(value=sentences_value,lines=10, label="Sentences",elem_id="inp_div")
+        hostname = get_device_ip_address()
+        url = 'https://pragnakalpdev35.pythonanywhere.com/hf_space_emotion_detection'
+        # url = 'http://pragnakalpdev33.pythonanywhere.com/HF_space_question_generator'
+        myobj = {'sen_list': sen_list,'gen_results': results,'ip_addr':hostname.get("ip_addr",""),'host':hostname.get("host","")}
+        x = requests.post(url, json = myobj) 
+            
+    except Exception as e:
+        return "Error while sending mail" + e
+        
+    return "Successfully save data"
+    
+"""
+UI design for demo using gradio app
+"""
+inputs = gr.Textbox(value=SENTENCES_VALUE,lines=10, label="Sentences",elem_id="inp_div")
 outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(2, "fixed"), label="Here is the Result", headers=["Input","Detected Emotion"])]
 
 demo = gr.Interface(