Create app.py

app.py

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+# Use a pipeline as a high-level helper
+from transformers import pipeline
+import torch
+import gradio as gr
+import pandas as pd
+
+#model_path = ("C:/Users/ankitdwivedi/OneDrive - Adobe/Desktop/NLP Projects/Video to Text Summarization/Model/models--distilbert--distilbert-base-uncased-finetuned-sst-2-english/snapshots/714eb0fa89d2f80546fda750413ed43d93601a13")
+
+analyzer = pipeline("text-classification", model="distilbert/distilbert-base-uncased-finetuned-sst-2-english")
+
+# print(analyzer(["This product is good", "This product is expensive"]))
+
+def sentiment_analyzer(review):
+    sentiment = analyzer(review)
+    return sentiment[0]['label']
+
+def Read_Analyze(file_object):
+    df = pd.read_csv(file_object, encoding='latin1')
+
+    if 'Review' not in df.columns:
+        raise ValueError("Review column not found")
+        df['Sentiment'] = df['Review'].apply(sentiment_analyzer)
+        return df
+
+# result = sentiment_analyzer("C:/Users/ankitdwivedi/OneDrive - Adobe/Desktop/NLP Projects/Video to Text Summarization/Files/all-data.csv")
+# print (result)
+
+gr.close_all()
+
+demo = gr.Interface(fn=Read_Analyze, 
+                    inputs=[gr.File(file_types = ["csv"], 
+                    label="Upload your review file")],outputs=[gr.Dataframe(label="Review")],
+                    title="Project 3: Sentiment Analyzer",
+                    description="""This is a Sentiment Analysis Model.""")
+demo.launch()