load the app with model

app.py

@@ -1,43 +1,57 @@
+from transformers import pipeline
 import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-
-# Load the pre-trained model and tokenizer
-tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
-model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
-
-# Define a function for sentiment analysis
-def predict_sentiment(text):
-    # Tokenize the input text and prepare it to be used by the model
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-
-    # Forward pass through the model
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    # Get the predicted probabilities and convert them to percentages
-    probabilities = torch.softmax(outputs.logits, dim=1).squeeze().tolist()
-    positive_percent = probabilities[2] * 100
-    negative_percent = probabilities[0] * 100
-    neutral_percent = probabilities[1] * 100
-
-    # Construct the result dictionary
-    result = {
-        "Positive": round(positive_percent, 2),
-        "Negative": round(negative_percent, 2),
-        "Neutral": round(neutral_percent, 2)
-    }
-
-    return result
-
-# Define inputs and outputs directly without using gr.inputs or gr.outputs
-iface = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.inputs.Textbox(lines=10, label="Enter financial statement"),
-    outputs=gr.outputs.Label(num_top_classes=3, label="Sentiment Percentages"),
-    title="Financial Statement Sentiment Analysis",
-    description="Predict the sentiment percentages of a financial statement."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+
+#load the model directly
+# Use a pipeline as a high-level helper
+pipe = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+
+
+#run the application
+demo=gr.Interface.from_pipeline(pipe)
+demo.launch()
+
+
+
+# import gradio as gr
+# from transformers import AutoModelForSequenceClassification, AutoTokenizer
+# import torch
+
+# # Load the pre-trained model and tokenizer
+# tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+# model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+
+# # Define a function for sentiment analysis
+# def predict_sentiment(text):
+#     # Tokenize the input text and prepare it to be used by the model
+#     inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+
+#     # Forward pass through the model
+#     with torch.no_grad():
+#         outputs = model(**inputs)
+
+#     # Get the predicted probabilities and convert them to percentages
+#     probabilities = torch.softmax(outputs.logits, dim=1).squeeze().tolist()
+#     positive_percent = probabilities[2] * 100
+#     negative_percent = probabilities[0] * 100
+#     neutral_percent = probabilities[1] * 100
+
+#     # Construct the result dictionary
+#     result = {
+#         "Positive": round(positive_percent, 2),
+#         "Negative": round(negative_percent, 2),
+#         "Neutral": round(neutral_percent, 2)
+#     }
+
+#     return result
+
+# # Define inputs and outputs directly without using gr.inputs or gr.outputs
+# iface = gr.Interface(
+#     fn=predict_sentiment,
+#     inputs=gr.inputs.Textbox(lines=10, label="Enter financial statement"),
+#     outputs=gr.outputs.Label(num_top_classes=3, label="Sentiment Percentages"),
+#     title="Financial Statement Sentiment Analysis",
+#     description="Predict the sentiment percentages of a financial statement."
+# )
+
+# if __name__ == "__main__":
+#     iface.launch()