Update app.py

app.py

@@ -13,7 +13,8 @@ import os
 import csv
 from gradio import inputs, outputs
 import huggingface_hub
-from huggingface_hub import Repository, hf_hub_download, upload_file
+from huggingface_hub import Repository, 
+hub_download, upload_file
 from datetime import datetime
 import fastapi
 from typing import List, Dict
@@ -58,6 +59,87 @@ Chatbot With persistent memory dataset allowing multiagent system AI to access a
   - [Language Models are Few-Shot Learners](https://paperswithcode.com/dataset/openwebtext) by Brown et al.
 """
 
+
+#Chatbot2 Save Results
+def SaveResult(text, outputfileName):
+    basedir = os.path.dirname(__file__)
+    savePath = outputfileName
+    print("Saving: " + text + " to " + savePath)
+    from os.path import exists
+    file_exists = exists(savePath)
+    if file_exists:
+        with open(outputfileName, "a") as f: #append
+            f.write(str(text.replace("\n","  ")))
+            f.write('\n')
+    else:
+        with open(outputfileName, "w") as f: #write
+            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
+            f.write(str(text.replace("\n","  ")))
+            f.write('\n')
+    return
+
+#Chatbot2 Store Message
+def store_message(name: str, message: str, outputfileName: str):
+    basedir = os.path.dirname(__file__)
+    savePath = outputfileName
+    
+    # if file doesnt exist, create it with labels
+    from os.path import exists
+    file_exists = exists(savePath)
+    
+    if (file_exists==False):
+        with open(savePath, "w") as f: #write
+            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
+            if name and message:
+                writer = csv.DictWriter(f, fieldnames=["time", "message", "name"])
+                writer.writerow(
+                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
+                )
+        df = pd.read_csv(savePath)
+        df = df.sort_values(df.columns[0],ascending=False)
+    else:
+        if name and message:
+            with open(savePath, "a") as csvfile:
+                writer = csv.DictWriter(csvfile, fieldnames=[ "time", "message", "name", ])
+                writer.writerow(
+                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
+                )
+        df = pd.read_csv(savePath)
+        df = df.sort_values(df.columns[0],ascending=False)
+    return df
+
+#Chatbot2 get base directory of saves
+def get_base(filename): 
+        basedir = os.path.dirname(__file__)
+        print(basedir)
+        loadPath = basedir + filename 
+        print(loadPath)
+        return loadPath
+
+#Chatbot2 - History
+def chat(message, history):
+    history = history or []
+    if history: 
+        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
+    else:
+        history_useful = []
+    history_useful = add_note_to_history(message, history_useful)
+    inputs = tokenizer(history_useful, return_tensors="pt")
+    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
+    reply_ids = model.generate(**inputs)
+    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
+    history_useful = add_note_to_history(response, history_useful)
+    list_history = history_useful[0].split('</s> <s>')
+    history.append((list_history[-2], list_history[-1]))  
+    df=pd.DataFrame()
+    if UseMemory: 
+        outputfileName = 'ChatbotMemory3.csv' # Test first time file create
+        df = store_message(message, response, outputfileName) # Save to dataset
+        basedir = get_base(outputfileName)
+    return history, df, basedir
+
+
+
 #ChatGPT predict
 def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  #repetition_penalty, top_k
 
@@ -141,6 +223,18 @@ def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):
                 history[-1] = partial_words
               chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
               token_counter+=1
+
+              
+              
+              df=pd.DataFrame()
+              if UseMemory: 
+                 outputfileName = 'ChatbotMemory3.csv' # Test first time file create
+                 df = store_message(inputs, chat, outputfileName) # Save to dataset
+                 basedir = get_base(outputfileName)
+              #return history, df, basedir
+
+              
+              
               yield chat, history, chat_counter  # resembles {chatbot: chat, state: history}  
 
 def take_last_tokens(inputs, note_history, history):
@@ -160,85 +254,6 @@ def add_note_to_history(note, note_history):# good example of non async since we
 def reset_textbox():
     return gr.update(value='')
 
-#Chatbot2 Save Results
-def SaveResult(text, outputfileName):
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    print("Saving: " + text + " to " + savePath)
-    from os.path import exists
-    file_exists = exists(savePath)
-    if file_exists:
-        with open(outputfileName, "a") as f: #append
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    else:
-        with open(outputfileName, "w") as f: #write
-            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
-            f.write(str(text.replace("\n","  ")))
-            f.write('\n')
-    return
-
-#Chatbot2 Store Message
-def store_message(name: str, message: str, outputfileName: str):
-    basedir = os.path.dirname(__file__)
-    savePath = outputfileName
-    
-    # if file doesnt exist, create it with labels
-    from os.path import exists
-    file_exists = exists(savePath)
-    
-    if (file_exists==False):
-        with open(savePath, "w") as f: #write
-            f.write(str("time, message, text\n")) # one time only to get column headers for CSV file
-            if name and message:
-                writer = csv.DictWriter(f, fieldnames=["time", "message", "name"])
-                writer.writerow(
-                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
-                )
-        df = pd.read_csv(savePath)
-        df = df.sort_values(df.columns[0],ascending=False)
-    else:
-        if name and message:
-            with open(savePath, "a") as csvfile:
-                writer = csv.DictWriter(csvfile, fieldnames=[ "time", "message", "name", ])
-                writer.writerow(
-                    {"time": str(datetime.now()), "message": message.strip(), "name": name.strip()  }
-                )
-        df = pd.read_csv(savePath)
-        df = df.sort_values(df.columns[0],ascending=False)
-    return df
-
-#Chatbot2 get base directory of saves
-def get_base(filename): 
-        basedir = os.path.dirname(__file__)
-        print(basedir)
-        loadPath = basedir + filename 
-        print(loadPath)
-        return loadPath
-
-#Chatbot2 - History
-def chat(message, history):
-    history = history or []
-    if history: 
-        history_useful = ['</s> <s>'.join([str(a[0])+'</s> <s>'+str(a[1]) for a in history])]
-    else:
-        history_useful = []
-    history_useful = add_note_to_history(message, history_useful)
-    inputs = tokenizer(history_useful, return_tensors="pt")
-    inputs, history_useful, history = take_last_tokens(inputs, history_useful, history)
-    reply_ids = model.generate(**inputs)
-    response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0]
-    history_useful = add_note_to_history(response, history_useful)
-    list_history = history_useful[0].split('</s> <s>')
-    history.append((list_history[-2], list_history[-1]))  
-    df=pd.DataFrame()
-    if UseMemory: 
-        outputfileName = 'ChatbotMemory3.csv' # Test first time file create
-        df = store_message(message, response, outputfileName) # Save to dataset
-        basedir = get_base(outputfileName)
-    return history, df, basedir
-
-
 # 6. Use Gradio to pull it all together
 with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin-right: auto;}   #chatbot {height: 520px; overflow: auto;}""") as demo:
                    
@@ -255,12 +270,12 @@ with gr.Blocks(css = """#col_container {width: 1000px; margin-left: auto; margin
     with gr.Row(): # inputs and buttons
        file = gr.File(label="File")
        s2 = gr.Markdown()
-    b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1, file]) 
+    #b1.click(fn=chat, inputs=[t1, s1], outputs=[s1, df1, file]) 
 
                     
     with gr.Column(elem_id = "col_container"):
         chatbot = gr.Chatbot(elem_id='chatbot')
-        inputs = gr.Textbox(placeholder= "There is only one real true reward in life and this is existence or nonexistence.  Everything else is a corollary.", label= "Type an input and press Enter") #t
+        inputs = gr.Textbox(placeholder= "There is only one real true reward in life and this is existence versus nonexistence.  Everything else is a corollary.", label= "Type an input and press Enter") #t
         state = gr.State([]) 
         gpt = gr.Button()