Update app.py

app.py

@@ -9,26 +9,23 @@ import plotly.express as px
 import plotly.graph_objects as go
 import streamlit as st
 import nltk
-import json
 import tempfile
 
-# process of getting credentials
+# Process of getting credentials
 def get_credentials():
-    creds_json_str = os.getenv("JSONSTR") # get json credentials stored as a string
+    creds_json_str = os.getenv("JSONSTR")  # Get json credentials stored as a string
     if creds_json_str is None:
         raise ValueError("GOOGLE_APPLICATION_CREDENTIALS_JSON not found in environment")
 
-    # create a temporary file
+    # Create a temporary file
     with tempfile.NamedTemporaryFile(mode="w+", delete=False, suffix=".json") as temp:
-        temp.write(creds_json_str) # write in json format
-        temp_filename = temp.name 
+        temp.write(creds_json_str)  # Write in json format
+        temp_filename = temp.name
 
     return temp_filename
-    
-# pass
-os.environ["GOOGLE_APPLICATION_CREDENTIALS"]= get_credentials()
-
 
+# Set the credentials
+os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = get_credentials()
 
 max_seq_length = 2048
 dtype = None
@@ -42,7 +39,6 @@ except LookupError:
 
 text_split_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')
 
-
 def predict_custom_trained_model_sample(
     project: str,
     endpoint_id: str,
@@ -50,10 +46,6 @@ def predict_custom_trained_model_sample(
     location: str = "us-east4",
     api_endpoint: str = "us-east4-aiplatform.googleapis.com",
 ) -> List[str]:
-    """
-    `instances` can be either single instance of type dict or a list
-    of instances.
-    """
     client_options = {"api_endpoint": api_endpoint}
     client = aiplatform.gapic.PredictionServiceClient(client_options=client_options)
     instances = instances if isinstance(instances, list) else [instances]
@@ -76,36 +68,30 @@ def predict_custom_trained_model_sample(
             split_predictions = clean_prediction.split()
             predictions_list.extend(split_predictions)
         else:
-            print(" prediction (unknown type, skipping):", prediction)
+            print("prediction (unknown type, skipping):", prediction)
     return [emotion for emotion in predictions_list if emotion in d_emotion.values()]
 
-
 d_emotion = {0: 'admiration', 1: 'amusement', 2: 'anger', 3: 'annoyance', 4: 'approval', 5: 'caring', 6: 'confusion',
              7: 'curiosity', 8: 'desire', 9: 'disappointment', 10: 'disapproval', 11: 'disgust', 12: 'embarrassment',
              13: 'excitement', 14: 'fear', 15: 'gratitude', 16: 'grief', 17: 'joy', 18: 'love', 19: 'nervousness',
              20: 'optimism', 21: 'pride', 22: 'realization', 23: 'relief', 24: 'remorse', 25: 'sadness', 26: 'surprise',
              27: 'neutral'}
 
-st.write(" ")
-st.write(" ")
-st.write("Write or paste any number of document texts to analyse the emotion percentage with your document")
+st.write("Write or paste any number of document texts to analyze the emotion percentage within your document")
+
+# Define user_input outside the conditional block
+user_input = ""
 
 # Add button to fill in sample text
 if st.button("Use Sample Text"):
-    user_input = st.text_area(label="Sample", value="Once, in a small village nestled in the rolling hills of Tuscany, lived an elderly woman named Isabella. She had spent her entire life in this village, raising her children and caring for her garden, which was the most beautiful in the region. Her husband, Marco, had passed away many years ago, leaving her with a heart full of memories and a small, quaint house that overlooked the lush vineyards.", disabled=True)
+    user_input = "Once, in a small village nestled in the rolling hills of Tuscany, lived an elderly woman named Isabella. She had spent her entire life in this village, raising her children and caring for her garden, which was the most beautiful in the region. Her husband, Marco, had passed away many years ago, leaving her with a heart full of memories and a small, quaint house that overlooked the lush vineyards."
 else:
     user_input = st.text_area('Enter Text to Analyze')
 
-
-# user_input = st.text_input(label, value=ur_input, height=None, max_chars=None, key=None, help=None, on_change=None, args=None, kwargs=None, *, placeholder=None, disabled=False, label_visibility="visible")
-
 button = st.button("Analyze")
 
-
-
-
 if user_input and button:
-    alpaca_prompt = """Below is a conversation between a human and an AI agent. write a response based on the input.
+    alpaca_prompt = """Below is a conversation between a human and an AI agent. Write a response based on the input.
         ### Instruction:
         predict the emotion word or words
         ### Input:
@@ -156,11 +142,9 @@ if user_input and button:
 
     fig_bar = get_emotion_chart(predictions)
 
-
     @st.cache_data
     def get_emotion_heatmap(predictions):
         # Create a matrix for heatmap
-        
         # Count occurrences of each emotion
         emotion_counts = pd.Series(predictions).value_counts().reset_index()
         emotion_counts.columns = ['Emotion', 'Count']
@@ -179,15 +163,13 @@ if user_input and button:
         ))
         fig.update_layout(title='Emotion Heatmap', xaxis_title='Predicted Emotion', yaxis_title='Predicted Emotion')
         return fig
-        
-    fig_dist = get_emotion_heatmap(predictions)
-    
-    tab1, tab2, tab3  = st.tabs(["Emotion Analysis", "Emotion Counts Distribution", "Heatmap"])
+
+    fig_heatmap = get_emotion_heatmap(predictions)
+
+    tab1, tab2, tab3 = st.tabs(["Emotion Analysis", "Emotion Counts Distribution", "Heatmap"])
     with tab1:
         st.plotly_chart(fig_pie)
     with tab2:
         st.plotly_chart(fig_bar)
     with tab3:
-        st.plotly_chart(fig_dist)
-
-        
+        st.plotly_chart(fig_heatmap)