Update app.py

app.py

@@ -1,31 +1,36 @@
 import streamlit as st
-from transformers import pipeline
+import openai
+import os
 
-# Load the pre-trained Finnish model
-model_name = "TurkuNLP/bert-base-finnish-cased-v1"
-nlp = pipeline("fill-mask", model=model_name)
+# Set your OpenAI API key here or set it as an environment variable
+openai.api_key = os.getenv("OPENAI_API_KEY", "sk-None-RVvMry6BpMfG8KxYS6hTT3BlbkFJSOfj2bCwA1EmTRrAHo5y")
 
-st.title("Finnish Language Understanding App")
+# Function to classify lines using GPT-3
+def classify_lines_with_gpt3(text):
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=f"Classify the following Finnish contract specifications into categories: Urakka sisältää: Urakka ei sisältää: Tilaajan velvoitteet:Käytäntöjen tarkennukset:Hintojen tarkennukset: Muu:.\n\n{text}\n\n",
+        max_tokens=1024,
+        n=1,
+        stop=None,
+        temperature=0.5,
+    )
+    
+    classified_text = response.choices[0].text.strip()
+    return classified_text
 
-st.write("This app demonstrates understanding of the Finnish language.")
+st.title("Finnish Contract Specifications Categorizer with GPT-3")
 
-# User input
-user_input = st.text_input("Enter a sentence in Finnish:")
+st.write("Enter the contract specifications in Finnish:")
 
-if user_input:
-    st.write("You entered:", user_input)
-    
-    # Use the model to predict masked words (as a simple example of language understanding)
-    masked_input = user_input.replace("____", "[MASK]")
-    results = nlp(masked_input)
+# Text area for large text input
+contract_text = st.text_area("Contract Specifications (Finnish):", height=300)
 
-    st.write("Predictions for the masked word:")
-    for result in results:
-        st.write(f"Prediction: {result['token_str']}, Score: {result['score']:.4f}")
+if st.button("Classify"):
+    if contract_text:
+        classified_text = classify_lines_with_gpt3(contract_text)
 
-if st.checkbox("Show example usage"):
-    st.write("Example sentence: Hän on ____ ystävä.")
-    example_results = nlp("Hän on [MASK] ystävä.")
-    st.write("Predictions for the masked word in the example:")
-    for result in example_results:
-        st.write(f"Prediction: {result['token_str']}, Score: {result['score']:.4f}")
+        st.write("Classified Contract Specifications:")
+        st.write(classified_text)
+    else:
+        st.write("Please enter the contract specifications.")