Update app.py

app.py

@@ -50,6 +50,19 @@ class GenerateResponse3(BaseModel):
 class GenerateResponse4(BaseModel):
     text: list
 
+# #Prepare the chat prompt 
+# chat_prompt = [
+#     {
+#         "role": "system",
+#         "content": [
+#             {
+#                 "type": "text",
+#                 "text": "The data is a pre-recorded conversation between a call center agent and a customer. Identify the following: keywords/metrics about the conversation, a summary of the conversation including details. Output should be in the format Keywords/Metrics: ,\n Summary:. \n Data: "
+#             }
+#         ]
+#     }
+# ] 
+
 # #Summarize function
 # def summarize_text(text: str):
 #         response = client.chat.completions.create(
@@ -91,7 +104,10 @@ def summarize_text(text: str):
         response = client.chat.completions.create(
             model = deployment_name,
             #engine="AmplifAI-Chat",
-            messages=["The data is a pre-recorded conversation between a call center agent and a customer. Identify the following: keywords/metrics about the conversation, a summary of the conversation including details. Output should be in the format Keywords/Metrics: ,\n Summary:. \n Data: " + text],
+            messages=[
+                {"role": "system", "content": "You are an AI assistant that summarizes conversations and extracts keywords/metrics."},
+                {"role": "user", "content": f"The data is a pre-recorded conversation between a call center agent and a customer. Identify the following: \n\n1. Keywords/Metrics about the conversation \n2. A summary of the conversation including details. \n\nOutput format: \nKeywords/Metrics: [Extracted Keywords]\nSummary: [Generated Summary]\n\nData: {text}"}
+            ],
             temperature=0.7,
             max_tokens=750,
             top_p=1.0,
@@ -100,7 +116,8 @@ def summarize_text(text: str):
         )
         if response.choices:
             # Extract the text from the response
-            summary = response.choices[0].text.strip()
+            # summary = response.choices[0].text.strip()
+            summary = response["choices"][0]["message"]["content"].strip()
             
             # Use regular expressions to find the keywords/metrics and summary sections
             keywords_match = re.search(r"Keywords/Metrics:(.*?)(\n|$)", summary, re.S)