Testing out loading in model

app.py

@@ -7,79 +7,14 @@ import nltk
 from nltk import sent_tokenize
 nltk.download("punkt")
 
-# Loading in dataframes
-krishnamurti_df = pd.read_json("krishnamurti_df.json")
-stoic_df = pd.read_json("stoic_df.json")
-
-# Loading in sentence_similarity model
-sentence_similarity_model = "all-mpnet-base-v2"
-model = SentenceTransformer(sentence_similarity_model) 
-
-# Loading in text-generation models
-stoic_generator = pipeline("text-generation", model="eliwill/stoic-generator-10e")
 krishnamurti_generator =  pipeline("text-generation", model="distilgpt2")
+# stoic_generator = pipeline("text-generation", model="eliwill/stoic-generator-10e")
 
-# Creating philosopher dictionary
-philosopher_dictionary = {
-    "stoic": {
-        "generator": stoic_generator,
-        "dataframe": stoic_df
-        },
-
-    "krishnamurti": {
-        "generator": krishnamurti_generator,
-        "dataframe": krishnamurti_df
-        }
-}
-
-############### DEFINING FUNCTIONS ###########################
-
-def ask_philosopher(philosopher, question):
-  """ Return first 5 sentences generated by question for the given philosopher model """
-
-  generator = philosopher_dictionary[philosopher]['generator']
-  answer = generator(question, min_length=100, max_length=120)[0]['generated_text'] # generate about 50 word tokens
-  answer = " ".join(sent_tokenize(answer)[:6]) # Get the first five sentences
-  return answer
-
-def get_similar_quotes(philosopher, question):
-  """ Return top 5 most similar quotes to the question from a philosopher's dataframe """
-  df = philosopher_dictionary[philosopher]['dataframe']
-  question_embedding = model.encode(question)
-  sims = [util.dot_score(question_embedding, quote_embedding) for quote_embedding in df['Embedding']]
-  ind = np.argpartition(sims, -5)[-5:]
-  similar_sentences = [df['quote'][i] for i in ind]
-  top5quotes = pd.DataFrame(data = similar_sentences, columns=["Quotes"], index=range(1,6))
-  top5quotes['Quotes'] = top5quotes['Quotes'].str[:-1].str[:250] + "..."
-  return top5quotes
-
-def main(question, philosopher):
-  return ask_philosopher(philosopher, question), get_similar_quotes(philosopher, question)
-  
-  
  ############### BUILDING DEMO ################################
 with gr.Blocks() as demo:
     gr.Markdown("""
     # Ask a Philsopher
     """
     )
-    with gr.Row():
-        with gr.Column():
-            inp1 = gr.Textbox(placeholder="Place your question here...", label="Ask a question")
-            inp2 = gr.Dropdown(choices=["stoic", "krishnamurti"], value="stoic", label="Choose a philosopher")
-
-        with gr.Column():
-            out1 = gr.Textbox(
-                        lines=3, 
-                        max_lines=10,
-                        label="Answer"
-                    )
-            out2 = gr.DataFrame(
-                        headers=["Quotes"],
-                        max_rows=5,
-                        interactive=False,
-                        wrap=True)
-    btn = gr.Button("Run")
-    btn.click(fn=main, inputs=[inp1,inp2], outputs=[out1,out2])
 
 demo.launch()