Create app.py

app.py

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+from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration
+import torch
+import gradio as gr
+
+import os
+import csv
+from gradio import inputs, outputs
+from datetime import datetime
+import fastapi
+from typing import List, Dict
+import httpx
+import pandas as pd
+import datasets as ds
+UseMemory=True
+
+HF_TOKEN=os.environ.get("HF_TOKEN")
+
+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
+
+    
+def store_message(name: str, message: str, outputfileName: str):
+    basedir = os.path.dirname(__file__)
+    savePath = outputfileName
+    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
+
+mname = "facebook/blenderbot-400M-distill"
+model = BlenderbotForConditionalGeneration.from_pretrained(mname)
+tokenizer = BlenderbotTokenizer.from_pretrained(mname)
+
+def take_last_tokens(inputs, note_history, history):
+    if inputs['input_ids'].shape[1] > 128:
+        inputs['input_ids'] = torch.tensor([inputs['input_ids'][0][-128:].tolist()])
+        inputs['attention_mask'] = torch.tensor([inputs['attention_mask'][0][-128:].tolist()])
+        note_history = ['</s> <s>'.join(note_history[0].split('</s> <s>')[2:])]
+        history = history[1:]
+    return inputs, note_history, history
+    
+def add_note_to_history(note, note_history):# good example of non async since we wait around til we know it went okay.
+    note_history.append(note)
+    note_history = '</s> <s>'.join(note_history)
+    return [note_history]
+
+title = "💬ChatBack🧠💾"
+description = """Chatbot With persistent memory dataset allowing multiagent system AI to access a shared dataset as memory pool with stored interactions. 
+ Current Best SOTA Chatbot:  https://huggingface.co/facebook/blenderbot-400M-distill?text=Hey+my+name+is+ChatBack%21+Are+you+ready+to+rock%3F  """
+
+def get_base(filename): 
+        basedir = os.path.dirname(__file__)
+        loadPath = basedir + "\\" + filename
+        return loadPath
+    
+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 = 'ChatbotMemory.csv'
+        df = store_message(message, response, outputfileName) # Save to dataset
+        basedir = get_base(outputfileName)
+        
+    return history, df, basedir
+
+    
+with gr.Blocks() as demo:
+  gr.Markdown("<h1><center>🍰Gradio chatbot backed by dataframe CSV memory🎨</center></h1>")
+  
+  with gr.Row():
+    t1 = gr.Textbox(lines=1, default="", label="Chat Text:")
+    b1 = gr.Button("Respond and Retrieve Messages")
+    
+  with gr.Row(): # inputs and buttons
+    s1 = gr.State([])
+    df1 = gr.Dataframe(wrap=True, max_rows=1000, overflow_row_behaviour= "paginate")
+  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]) 
+    
+demo.launch(debug=True, show_error=True)