Completion from messages

app.py

@@ -8,29 +8,38 @@ _ = load_dotenv(find_dotenv())
 
 openai.api_key  = os.getenv('OPENAI_API_KEY')
 
-def get_completion(prompt, model="gpt-3.5-turbo"):
-    messages = [{"role": "user", "content": prompt}]
+def get_completion_from_messages(messages,
+                                 model="gpt-3.5-turbo",
+                                 temperature=0,
+                                 max_tokens=500):
     response = openai.ChatCompletion.create(
         model=model,
         messages=messages,
-        temperature=0, # this is the degree of randomness of the model's output
+        temperature=temperature, # this is the degree of randomness of the model's output
+        max_tokens=max_tokens, # the maximum number of tokens the model can ouptut
     )
     return response.choices[0].message["content"]
 
-def greet(company, solution, target_customer, problem, features):
+def greet(company, solution, target_customer, problem, features, customer_persona="the target customer"):
     pitch = f"""My company, {company} is developing {solution} to help {target_customer} {problem} with {features}"""
-    prompt = f"""
+
+sys_setup = f"""
 Determine the product or solution, the problem being solved, features, target customer that are being discussed in the \
-following text, which is delimited by triple backticks. Then, pretend that you are the target customer. \
-State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.\
+following user prompt. State if you would use this product and elaborate on why. Also state if you would pay for it and elaborate on why.\
 Give a score for the product.
 
 Format your response as a JSON object with \
 'solution', 'problem', 'features', 'target_customer', 'fg_will_use', 'reason_to_use', 'fg_will_pay', 'reason_to_pay', 'score' as the keys.
-
-Text sample: '''{pitch}'''
 """
-    response = get_completion(prompt)
+    messages =  [
+    {'role':'system',
+    'content':"You are " + customer_persona + "."},
+    {'role':'system',
+    'content': sys_setup},
+    {'role':'user',
+    'content':pitch},
+    ]
+    response = get_completion_from_messages(messages, temperature=1)
     return json.dumps(response)
 
 iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Company"), gr.Textbox(label="Solution"), gr.Textbox(label="Customer"), gr.Textbox(label="Problem"),  gr.Textbox(label="Feature")], outputs="json")