Update utils.py

utils.py

@@ -11,15 +11,30 @@ from transformers import pipeline
 from openai import OpenAI
 from groq import Groq
 import time 
+from openai import OpenAI
 
 #openai_key = "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"
 #client = OpenAI(api_key = openai_key)
 desc = pd.read_excel('Descriptor.xlsx',header = None)
 desc_list  = desc.iloc[:,0].to_list()
-  
-model = "llama3-70b-8192"
-pipe = pipeline("text-classification", model="mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
 
+def callAzure(prompt,text):
+    
+    url = "https://Meta-Llama-3-70B-Instruct-fkqip-serverless.eastus2.inference.ai.azure.com"
+    api_key = "o5yaLhTIvg0s5zuYVInBpyneEZO8oonY"
+    client = OpenAI(base_url=url, api_key=api_key)
+    response = client.chat.completions.create(
+        messages=[
+            {
+                "role": "user",
+                "content": "{} {}".format(prompt, text),
+            }
+        ],
+        model="azureai",
+        ) 
+    
+    return response.choices[0].message.content
+    
 def call(prompt, text):
   client = Groq(api_key=os.getenv("key"),)
 
@@ -85,15 +100,15 @@ def summary(input_json):
     
     output["Stock Ticker"] = input_json['symbol']
     
-    output['Short Summary'] = call(promptShort[id], long_text)
+    output['Short Summary'] = callAzure(promptShort[id], long_text)
 
-    output['Long summary'] = call(promptLong[id], long_text)
+    output['Long summary'] = callAzure(promptLong[id], long_text)
 
     prompt = "1 word Financial SEO tag for this news article"
-    output['Tag'] = call(prompt, output['Short Summary'])
+    output['Tag'] = callAzure(prompt, output['Short Summary'])
 
     prompt = "Give a single headline for this News Article"
-    output['Headline'] = call(prompt, output['Short Summary'])
+    output['Headline'] = callAzure(prompt, output['Short Summary'])
 
     utc_now = datetime.datetime.utcnow()
     ist_now = utc_now.astimezone(datetime.timezone(datetime.timedelta(hours=5, minutes=30)))
@@ -103,9 +118,9 @@ def summary(input_json):
     output['Date and time of data delivery from Skylark'] = Date+"T"+time+"Z"
     
     prompt = "Answer in one word the sentiment of this News out of Positive, Negative or Neutral {}"
-    output['Sentiment'] = call(prompt, output['web'])
+    output['Sentiment'] = callAzure(prompt, output['Short Summary'])
 
-    time.sleep(60)
+    #time.sleep(60)
     # response = client.images.generate(
     # model="dall-e-3",
     # prompt=headline.text,