Update model.py

model.py

@@ -1,37 +1,52 @@
-import streamlit as st
-from model import GeneralModel
-
-
-def app():
-
-    # Creating an object of prediction service
-    pred = GeneralModel()
-
-    api_key = st.sidebar.text_input("APIkey", type="password")
-    # Using the streamlit cache
-    @st.cache
-    def process_prompt(input):
-
-        return pred.model_prediction(input=input.strip() , api_key=api_key)
-
-    if api_key:
-
-        # Setting up the Title
-        st.title("Escritor GPT-3")
-
-        # st.write("---")
-
-        s_example = "Escriba un ensayo argumentativo a favor de los vouchers escolares"
-        input = st.text_area(
-            "Use el ejemplo de abajo o escriba su propio texto en español",
-            value=s_example,
-            max_chars=1250,
-            height=50,
-        )
-
-        if st.button("Submit"):
-            with st.spinner(text="In progress"):
-                report_text = process_prompt(input)
-                st.markdown(report_text)
-    else:
-        st.error("🔑 Please enter API Key")
+import openai
+
+poem = """Escriba lo que se le pide: 
+---
+{input}
+---
+Este es el resultado: """
+
+def set_openai_key(key):
+    """Sets OpenAI key."""
+    openai.api_key = key
+
+class GeneralModel:
+    def __init__(self):
+        print("Model Intilization--->")
+        # set_openai_key(API_KEY)
+
+    def query(self, prompt, myKwargs={}):
+        """
+        wrapper for the API to save the prompt and the result
+        """
+
+        # arguments to send the API
+        kwargs = {
+            "engine": "text-davinci-002",
+            "temperature": 0.85,
+            "max_tokens": 2400,
+            "best_of": 1,
+            "top_p": 1,
+            "frequency_penalty": 0.5,
+            "presence_penalty": 0.5,
+            "stop": ["###"],
+        }
+
+
+        for kwarg in myKwargs:
+            kwargs[kwarg] = myKwargs[kwarg]
+
+
+        r = openai.Completion.create(prompt=prompt, **kwargs)["choices"][0][
+            "text"
+        ].strip()
+        return r
+
+    def model_prediction(self, input, api_key):
+        """
+        wrapper for the API to save the prompt and the result
+        """
+        # Setting the OpenAI API key got from the OpenAI dashboard
+        set_openai_key(api_key)
+        output = self.query(poem.format(input = input))
+        return output