change from openai to phi

pages/summarization_example.py

@@ -30,17 +30,25 @@ summarization: """  # noqa: E501
         sources=sources,
     )
 
-    answer = lm.chat.completions.create(
-        temperature=0.8,
-        max_tokens=800,
-        messages=[
-            {
-                "role": "user",
-                "content": content,
-            },
-        ],
-        model=model_name,
-    )
+    from transformers import pipeline
+
+    messages = [
+        {"role": "user", "content": content},
+    ]
+    pipe = pipeline("text-generation", model="microsoft/Phi-3-mini-4k-instruct", trust_remote_code=True)
+    answer = pipe(messages)
+
+    # answer = lm.chat.completions.create(
+    #     temperature=0.8,
+    #     max_tokens=800,
+    #     messages=[
+    #         {
+    #             "role": "user",
+    #             "content": content,
+    #         },
+    #     ],
+    #     model=model_name,
+    # )
 
     return answer
 
@@ -58,11 +66,11 @@ input_text1 = st.text_area("question", height=None, \
 
 
 # Button to trigger processing
-lm = OpenAI()
+#lm = OpenAI()
 
 if st.button('Submit'):
     if input_text1:
-        response = generate_answer(lm, input_text1, model_selection)
+        response = generate_answer('', input_text1, model_selection)
         st.write('## Orginal Article:')
         st.markdown(examples[example_selection])
 

utils/multiple_stream.py

@@ -7,7 +7,7 @@ import streamlit as st
 from streamlit.runtime.scriptrunner.script_run_context import \
     add_script_run_ctx
 
-_TEST = """
+_TEST_ = """
 Test of Time. A Benchmark for Evaluating LLMs on Temporal Reasoning. Large language models (LLMs) have \
 showcased remarkable reasoning capabilities, yet they remain susceptible to errors, particularly in temporal \
 reasoning tasks involving complex temporal logic.
@@ -15,7 +15,7 @@ reasoning tasks involving complex temporal logic.
 
 def generate_data_test():
     """A generator to pass to st.write_stream"""
-    temp = copy.deepcopy(_TEST)
+    temp = copy.deepcopy(_TEST_)
     l1 = temp.split()
     random.shuffle(l1)
     temp = ' '.join(l1)