Update app.py

app.py

@@ -2,9 +2,9 @@
 #   app.py   -  Pennwick Honeybee Robot
 #
 #   HuggingFace Spaces application to provide honeybee expertise
-#           with open-source models ( hkunlp/instructor-xl )
+#           with open-source models
 #
-#   Mike Pastor  February 21, 2024
+#   Mike Pastor  February 22, 2024
 
 
 import streamlit as st
@@ -35,8 +35,9 @@ DISPLAY_DIALOG_LINES = 6
 SESSION_STARTED = False
 
 # MODEL_NAME="deepset/roberta-base-squad2"
+#MODEL_NAME="BEE-spoke-data/TinyLlama-3T-1.1bee"
 
-MODEL_NAME="BEE-spoke-data/TinyLlama-3T-1.1bee"
+MODEL_NAME='HuggingFaceH4/zephyr-7b-beta'
 
 ##################################################################################
 def extract_pdf_text(pdf_docs):
@@ -185,35 +186,68 @@ def process_user_question(user_question):
     #
     #         results_string += ("<p>" + "-- " + message.content + "</p>")
 
-    st.write(('Question: ' + user_question), unsafe_allow_html=True)
 
-    from transformers import pipeline
-
-    # Choose a question answering pipeline (e.g., 'question-answering')
-    nlp = pipeline("question-answering")
+    #################################################################
+    #  Track the overall time for training & submission preparation
+    # #
+    from datetime import datetime
+    global_now = datetime.now()
+    global_current_time = global_now.strftime("%H:%M:%S")
+    print("# app.py Starting up...  - Current Time =", global_current_time)
+    st.write(('Question: ' + user_question + ' | ' + str( global_current_time )), unsafe_allow_html=True)
 
-    # Specify the model name or identifier (e.g., 'deepset/roberta-base-squad2')
-    model_name = MODEL_NAME
+    from transformers import pipeline
 
-    # Prepare the question and context (optional)
-    # question = "What is the capital of France?"
-    # context = "France is a country located in Western Europe. It is bordered by the Atlantic Ocean to the west, the Mediterranean Sea to the south, and Belgium, Luxembourg, Germany, Switzerland, Italy, and Spain to the east and north."
+    pipe = pipeline("conversational", "HuggingFaceH4/zephyr-7b-beta")
 
-    context = "You are an expert Apiarist and answer all questions regarding Honeybees."
-    # context = " "
+    # "Question: which technology is most recent?   A)Cell phones  B)Television C)Airplane  Answer: "
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a friendly chatbot who always responds in the style of a personal assistant",
+        },
+        {"role": "user",
+         "content": user_question },
+    ]
 
-    # Ask the question
-    answer = nlp(question= ('Question: '+user_question), context=context, model=model_name)
+    st.write('Sending message to Model->  ', messages )
+    results_string = pipe(messages)
 
-    # Print the answer
-    print(f"Answer: {answer['answer']}")
-    print(f"Score: {answer['score']}")
+    # st.write('results type= ', type( results_string) )
+    st.write('results= ', results_string)
+    # st.write('results ans= ', results_string[0])
 
-    st.write( ('Answer= '+answer['answer']), unsafe_allow_html=True)
+    # Mission Complete!
+    ##################################################################################
+    global_later = datetime.now()
+    st.write("# Total EXECUTION Time =", (global_later - global_now), global_later)
 
-    results_string = answer['answer'] + '     - Probability= ' + str( answer['score'] )
+    #
+    # # Choose a question answering pipeline (e.g., 'question-answering')
+    # nlp = pipeline("question-answering")
+    #
+    # # Specify the model name or identifier (e.g., 'deepset/roberta-base-squad2')
+    # model_name = MODEL_NAME
+    #
+    # # Prepare the question and context (optional)
+    # # question = "What is the capital of France?"
+    # # context = "France is a country located in Western Europe. It is bordered by the Atlantic Ocean to the west, the Mediterranean Sea to the south, and Belgium, Luxembourg, Germany, Switzerland, Italy, and Spain to the east and north."
+    #
+    # context = "You are an expert Apiarist and answer all questions regarding Honeybees."
+    # # context = " "
+    #
+    # # Ask the question
+    # answer = nlp(question= ('Question: '+user_question), context=context, model=model_name)
+    #
+    # # Print the answer
+    # print(f"Answer: {answer['answer']}")
+    # print(f"Score: {answer['score']}")
+    #
+    # st.write( ('Answer= '+answer['answer']), unsafe_allow_html=True)
+    #
+    # results_string = answer['answer'] + '     - Probability= ' + str( answer['score'] )
 
-    html(results_string, height=100, scrolling=True)
+    #  html(results_string, height=300, scrolling=True)
 
 
 ###################################################################################