Upload 2 files

app.py

@@ -0,0 +1,62 @@
+#import csv
+import gradio as gr
+import pandas as pd
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig
+#from datasets import load_dataset
+
+
+
+# Load the model and define the sentiment classifier
+MODEL = "LiYuan/amazon-review-sentiment-analysis"
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+pipe = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer, config=config)
+
+
+def classify_sentiment(sentences):
+  # Classify the sentiment of each sentence
+  predictions = pipe(sentences)
+  
+  # Extract the predicted labels and confidence scores from the predictions
+  labels = [prediction['label'] for prediction in predictions]
+  confidences = [prediction['score'] for prediction in predictions]
+  
+  return labels, confidences
+
+def classify_sentiment_from_csv(csv_file):
+  # Read the CSV file and extract the list of sentences
+  df = pd.read_csv(csv_file.name, delimiter=",")
+  sentences = df['sentence'].tolist()
+  
+  # Classify the sentiment of the sentences
+  labels, confidences = classify_sentiment(sentences)
+  df['confidences'] = confidences
+  df['labels'] = labels
+  return df
+
+# Define the gradio app
+def main():
+  iface = gr.Interface(fn=classify_sentiment_from_csv,
+                       inputs=gr.File(),
+                       outputs=gr.Dataframe(),
+                       live=True,
+                       #capture_session=True,
+                       allow_flagging='never')
+  
+  iface.launch(enable_queue=False)
+
+
+
+#debug:
+# labels, confidence = classify_sentiment_from_csv("./reviews.csv")
+# print(labels)
+
+
+
+# Run the gradio app
+if __name__ == "__main__":
+  main()
+
+

requirements.txt

@@ -0,0 +1,6 @@
+
+diffusers
+#transformers
+git+https://github.com/huggingface/transformers
+transformers
+pandas