Update app.py

app.py

@@ -20,38 +20,63 @@ class SentimentAnalyzer:
        pass
 
     def emotion_analysis(self,text):
-        prompt = f""" Your task is find the top 3 emotion for this converstion {text}: <Sadness, Happiness, Fear, Disgust, Anger> and it's emotion score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
+        # Create a conversation for the OpenAI chat API
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f""" Your task is find the top 3 emotion for this converstion {text}: <Sadness, Happiness, Fear, Disgust, Anger> and it's emotion score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
         you 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 3 result having the highest score]
         The scores should be in the range of 0.0 to 1.0, where 1.0 represents the highest intensity of the emotion.
-        """
-        response = client.completions.create(
-            model="text-davinci-003",
-            prompt=prompt,
-            temperature=0,
-            max_tokens=60,
-            top_p=1,
-            frequency_penalty=0,
-            presence_penalty=0
+        """}
+        ]
+        
+        # Call OpenAI GPT-3.5-turbo
+        chat_completion = client.chat.completions.create(
+            model = "gpt-3.5-turbo-1106",
+            messages = conversation,
+            max_tokens=500,
+            temperature=0
         )
-        message = response.choices[0].text
-        return message
+        
+        response = chat_completion.choices[0].message.content
+        return response
+
 
     def analyze_sentiment_for_graph(self, text):
-        prompt = f""" Your task is find the setiments for this converstion {text} : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
+
+        # Create a conversation for the OpenAI chat API
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f""" Your task is find the setiments for this converstion {text} : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
         you are analyze the text and provide the output in the following json format heigher to lower order: '''["label1","label2","label3"][score1,score2,score3]'''
-        """
-        response = client.completions.create(
-        model="text-davinci-003",
-        prompt=prompt,
-        temperature=0,
-        max_tokens=60,
-        top_p=1,
-        frequency_penalty=0,
-        presence_penalty=0
+        """}
+        ]
+        
+        # Call OpenAI GPT-3.5-turbo
+        chat_completion = client.chat.completions.create(
+            model = "gpt-3.5-turbo-1106",
+            messages = conversation,
+            max_tokens=500,
+            temperature=0
         )
+        
+        response = chat_completion.choices[0].message.content
+        return response
+        
+        # prompt = f""" Your task is find the setiments for this converstion {text} : <labels = positive, negative, neutral> and it's sentiment score for the Mental Healthcare Doctor Chatbot and patient conversation text.\
+        # you are analyze the text and provide the output in the following json format heigher to lower order: '''["label1","label2","label3"][score1,score2,score3]'''
+        # """
+        # response = client.completions.create(
+        # model="text-davinci-003",
+        # prompt=prompt,
+        # temperature=0,
+        # max_tokens=60,
+        # top_p=1,
+        # frequency_penalty=0,
+        # presence_penalty=0
+        # )
 
         # Extract the generated text
-        sentiment_scores = response.choices[0].text.strip()
+        sentiment_scores = response
         start_index = sentiment_scores.find("[")
         end_index = sentiment_scores.find("]")
         list1_text = sentiment_scores[start_index + 1: end_index]
@@ -79,16 +104,24 @@ class Summarizer:
         # openai.api_key=os.getenv("OPENAI_API_KEY")
         pass
     def generate_summary(self, text):
-        completions = client.completions.create(
-            model="text-davinci-003",
-            prompt=f"""summarize the following conversation delimited by triple backticks. write within 30 words.```{text}``` """,
-            max_tokens=60,
-            n=1,
-            stop=None,
-            temperature=0.5,
+
+        # Create a conversation for the OpenAI chat API
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f"""summarize the following conversation delimited by triple backticks. write within 30 words.```{text}``` """}
+        ]
+        
+        # Call OpenAI GPT-3.5-turbo
+        chat_completion = client.chat.completions.create(
+            model = "gpt-3.5-turbo-1106",
+            messages = conversation,
+            max_tokens=500,
+            temperature=0
         )
-        message = completions.choices[0].text
-        return message
+        
+        response = chat_completion.choices[0].message.content
+        return response
+        
 
 history_state = gr.State()
 summarizer = Summarizer()
@@ -161,8 +194,11 @@ class LangChain_Document_QA:
               patient_details = json.load(file)
         except:
           pass
-
-        prompt = f"""Analyse the patient json If asked for information take it from {patient_details} \
+            
+        # Create a conversation for the OpenAI chat API
+        conversation = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": f"""Analyse the patient json If asked for information take it from {patient_details} \
             you first get patient details : <get name,age,gender,contact,address from patient> if not match patient json information start new chat else match patient \
             json information ask previous: <description,symptoms,diagnosis,treatment talk about patient> As an empathic AI Mental Healthcare Doctor Chatbot, provide effective solutions to patients' mental health concerns. \
             first start the conversation ask existing patient or new patient. if new patient get name,age,gender,contact,address from the patient and start. \
@@ -171,19 +207,20 @@ class LangChain_Document_QA:
             Chat History:['''{history_list}'''] 
             Patient: ['''{text}''']
             Perform as Mental Healthcare Doctor Chatbot
-                 """
-        response = client.completions.create(
-            model="text-davinci-003",
-            prompt=prompt,
-            temperature=0,
+                 """}
+        ]
+        
+        # Call OpenAI GPT-3.5-turbo
+        chat_completion = client.chat.completions.create(
+            model = "gpt-3.5-turbo-1106",
+            messages = conversation,
             max_tokens=500,
-            top_p=1,
-            frequency_penalty=0,
-            presence_penalty=0.6,
+            temperature=0
         )
-
-        message = response.choices[0].text.strip()
-        if  ":" in message:
+        
+        response = chat_completion.choices[0].message.content
+        
+        if  ":" in response:
           message = re.sub(r'^.*:', '', message)
         history[-1][1] = message.strip()
         history_state.value = history