Update app.py

app.py

@@ -34,28 +34,21 @@ def predict(text=None , fil=None):
     fig=None
     if fil:
       if fil.name.endswith('.csv'):
-          df = pd.read_csv(fil.name)
+          df = pd.read_csv(fil.name, header=None)
       elif fil.name.endswith('.xlsx') or fil.name.endswith('.xls'):
-          df = pd.read_excel(fil.name)
+          df = pd.read_excel(fil.name, header=None)
       else:
           raise ValueError("Unsupported file type. Please upload a CSV or Excel file.")
 
-      # df= df.sample(20)
-      lst = list(df.tweet)
+   
+      lst = list(df[0])
       m =[normalizeTweet(i) for i in lst]
-      # m = [truncate_string(i) for i in m]
+
       d = pd.DataFrame(pipe.predict(m))
       df['label'] = d['label']
-      # print(df.sample(5))
-      df.drop('sarcastic', axis=1, inplace=True)
-      # print(df.sample(5))
 
-      mapping = {
-          'LABEL_0': 'non_sarcastic',
-          'LABEL_1': 'sarcastic'
-      }
 
-      # df['label']=df['label'].map(mapping)
+
       sarcastic_count = np.sum(df.label =='sarcastic')
       non_sarcastic_count = np.sum(df.label =='non_sarcastic')
 
@@ -70,18 +63,11 @@ def predict(text=None , fil=None):
 
       plt.title('Sarcastic vs Non-Sarcastic Tweets')
 
-      # fig = plt.figure()  #figsize=(8, 6)
-      # sns.countplot(x='label', data=df, palette='viridis')
-      # plt.title('Result: Count Plot')  # Add a title to the plot
-      # plt.xlabel('label')  # Add label for the x-axis
-      # plt.ylabel('Count')
-     # Perform sentiment prediction
+  
     if text !="" or fil !=None:
       prediction = pipe.predict([preprocessed_text])[0]
       print(prediction)
-      # sentiment = {p['label']: p['score'] for p in prediction}
-      # sentiment['']
-      # print(sentiment)
+     
       sentiment = "Sarcastic" if (prediction['label'] == 'LABEL_1' or prediction['label'] =='sarcastic') else "Non Sarcastic"
       if fil == None:
         df= pd.DataFrame([{'tweet':text, 'label':sentiment}])
@@ -110,18 +96,19 @@ def classifyB(text=None , fil=None):
     sentiment =None
     df=None
     fig=None
+    
     labels = ['sarcasm', 'irony','Staire', 'understatement','overstatement', 'rhetorical question']
+    
     if fil:
       if fil.name.endswith('.csv'):
-          df = pd.read_csv(fil.name)
+          df = pd.read_csv(fil.name, header=None)
       elif fil.name.endswith('.xlsx') or fil.name.endswith('.xls'):
-          df = pd.read_excel(fil.name)
+          df = pd.read_excel(fil.name, header=None)
       else:
           raise ValueError("Unsupported file type. Please upload a CSV or Excel file.")
 
-      lst = list(df.tweet)
+      lst = list(df[0])
       m =[normalizeTweet(i) for i in lst]
-      # m = [truncate_string(i) for i in m]
       d = pipe2(m)
 
       structured_data = []
@@ -136,32 +123,26 @@ def classifyB(text=None , fil=None):
       df1 = pd.DataFrame(structured_data)
       df = pd.concat([df, df1], axis=1)
 
-      # df["labels"] = d['labels']
-      # print("df: ",df.head())
-      # return df.head()
-
 
       fig = plt.figure()  #figsize=(8, 6)
       sns.countplot(x='label', data=df, palette='viridis')
       plt.title('Result: Count Plot')  # Add a title to the plot
       plt.xlabel('label')  # Add label for the x-axis
       plt.ylabel('Count')
+      if text == None:
+        sentiment = df['label'][0]
+        
      # Perform sentiment prediction
-    if text !=None or fil !=None:
+    if text != None:
       prediction = pipe2([preprocessed_text])[0]
-      print(prediction["label"])
+      # print(prediction["label"])
       labels = prediction['label']
-      scores = prediction['score']
-
-      # Combine labels and scores, and sort by score in descending order
-
-
-      # Extract top 3 labels and their scores
-
+      # scores = prediction['score']
       sentiment = labels
+    
+    return sentiment, df, fig
 
 
-    return sentiment, df, fig
 
 file_path =gr.File(label="Upload a File")
 label = gr.Label( label="Labels")