Upload 4 files

app (8).py

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+from transformers.tools.base import launch_gradio_demo
+from sentiment_analysis import SentimentAnalysisTool
+
+launch_gradio_demo(SentimentAnalysisTool)

requirements (4).txt

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+transformers>=4.29.0
+gradio
+#diffusers
+#accelerate
+torch

sentiment_analysis.py

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+import requests
+import gradio as gr
+from transformers import pipeline
+from transformers import Tool
+
+class SentimentAnalysisTool(Tool):
+    name = "sentiment_analysis"
+    description = "This tool analyses the sentiment of a given text input."
+
+    inputs = ["text"]  # Adding an empty list for inputs
+    outputs = ["json"]
+    
+    model_id_1 = "nlptown/bert-base-multilingual-uncased-sentiment"
+    model_id_2 = "microsoft/deberta-xlarge-mnli"
+    model_id_3 = "distilbert-base-uncased-finetuned-sst-2-english"
+    model_id_4 = "lordtt13/emo-mobilebert"
+    model_id_5 = "juliensimon/reviews-sentiment-analysis"
+    model_id_6 = "sbcBI/sentiment_analysis_model"
+    model_id_7 = "models/oliverguhr/german-sentiment-bert"
+    
+    def __call__(self, inputs: str): 
+        return self.predicto(inputs)
+        
+    def parse_output(self, output_json):  
+        list_pred = []
+        for i in range(len(output_json[0])):
+            label = output_json[0][i]['label']
+            score = output_json[0][i]['score']
+            list_pred.append((label, score))
+        return list_pred
+    
+    def get_prediction(self, model_id):
+        classifier = pipeline("text-classification", model=model_id, return_all_scores=True)
+        return classifier
+    
+    def predicto(self, review):
+        classifier = self.get_prediction(self.model_id_3)
+        prediction = classifier(review)
+        print(prediction)
+        return self.parse_output(prediction)
+
+# Create an instance of the SentimentAnalysisTool class
+sentiment_analysis_tool = SentimentAnalysisTool()
+
+# Create the Gradio interface
+gr.Interface(fn=sentiment_analysis_tool, inputs=sentiment_analysis_tool.inputs, outputs=sentiment_analysis_tool.outputs).launch()

tool_config.json

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+{
+  "description": "Based on a text input this tool is able to analyse the sentiment of the given text. It returns a json with the sentiment.",
+  "name": "sentiment_analysis",
+  "tool_class": "sentiment_analysis.SentimentAnalysisTool"
+}