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Karthikeyan
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3227853
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Parent(s):
eaed23c
Update app.py
Browse files
app.py
CHANGED
@@ -25,7 +25,7 @@ class SentimentAnalyzer:
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sentiment_scores_str = f"Positive: {sentiment_scores['positive']:.2f}, Neutral: {sentiment_scores['neutral']:.2f}, Negative: {sentiment_scores['negative']:.2f}"
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return sentiment_scores_str
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def emotion_analysis(self,text):
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prompt = f""" Your task is find the top
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your are analyze the text and provide the output in the following list format heigher to lower order: ["emotion1","emotion2","emotion3"][score1,score2,score3]''' [with top 1 result having the highest score]
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The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.\
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analyze the text : '''{text}'''
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@@ -39,9 +39,7 @@ class SentimentAnalyzer:
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frequency_penalty=0,
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presence_penalty=0
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)
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message = response.choices[0].text.strip().replace("\n","")
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print(message)
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return message
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def analyze_sentiment_for_graph(self, text):
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@@ -72,9 +70,7 @@ class Summarizer:
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openai.api_key=os.getenv("OPENAI_API_KEY")
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def generate_summary(self, text):
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model_engine = "text-davinci-003"
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prompt = f"""summarize the following conversation delimited by triple backticks.
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write within 30 words.
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```{text}``` """
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completions = openai.Completion.create(
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engine=model_engine,
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prompt=prompt,
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sentiment_scores_str = f"Positive: {sentiment_scores['positive']:.2f}, Neutral: {sentiment_scores['neutral']:.2f}, Negative: {sentiment_scores['negative']:.2f}"
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return sentiment_scores_str
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def emotion_analysis(self,text):
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prompt = f""" Your task is find the top 3 emotion : <Sadness, Happiness, Joy, Fear, Disgust, Anger> and it's emotion score of the Mental Healthcare Doctor Chatbot and patient conversation text.\
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your are analyze the text and provide the output in the following list format heigher to lower order: ["emotion1","emotion2","emotion3"][score1,score2,score3]''' [with top 1 result having the highest score]
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The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.\
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analyze the text : '''{text}'''
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frequency_penalty=0,
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presence_penalty=0
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)
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message = response.choices[0].text.strip().replace("\n","")
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return message
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def analyze_sentiment_for_graph(self, text):
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openai.api_key=os.getenv("OPENAI_API_KEY")
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def generate_summary(self, text):
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model_engine = "text-davinci-003"
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prompt = f"""summarize the following conversation delimited by triple backticks. write within 30 words.```{text}``` """
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completions = openai.Completion.create(
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engine=model_engine,
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prompt=prompt,
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