chore: fix

src/agent/tools/conversation.py

@@ -30,27 +30,39 @@ class Conversation():
     #     bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1)
     #     chat_history_ids =self.model.generate(bot_input_ids, max_length=1000, pad_token_id=self.tokenizer.eos_token_id)
     #     return "{}".format(self.tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))
-
-    def generate(self, instruction, knowledge, dialog):
-        if knowledge != '':
-            knowledge = '[KNOWLEDGE] ' + knowledge
-        dialog = ' EOS '.join(dialog)
-        query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
-        input_ids = self.tokenizer(f"{query}", return_tensors="pt").input_ids
-        outputs = self.model.generate(
-            input_ids, max_length=128,
-            min_length=8,
-            top_p=0.9,
-            do_sample=True,
-        )
-        output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
-        return output
+    def predict(self, input, history=[]):
+       instruction = "Instruction: given a dialog context and related knowledge, you need to answer the question based on the knowledge."
+       knowledge = '[KNOWLEDGE] '
+       s = list(sum(history, ()))
+       s.append(input)
+       dialog = ' EOS '.join(s)
+       query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+       input_ids = self.tokenizer.encode(f"{query}", return_tensors='pt')
+       print(input, s)
+       output = self.model.generate(input_ids, max_length=128, min_length=8, top_p=0.9, do_sample=True).tolist()
+       response = self.tokenizer.decode(output[0], skip_special_tokens=True)
+       history.append((input, response))
+       return response
+    # def generate(self, instruction, knowledge, dialog):
+    #     if knowledge != '':
+    #         knowledge = '[KNOWLEDGE] ' + knowledge
+    #     dialog = ' EOS '.join(dialog)
+    #     query = f"{instruction} [CONTEXT] {dialog} {knowledge}"
+    #     input_ids = self.tokenizer(f"{query}", return_tensors="pt").input_ids
+    #     outputs = self.model.generate(
+    #         input_ids, max_length=128,
+    #         min_length=8,
+    #         top_p=0.9,
+    #         do_sample=True,
+    #     )
+    #     output = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+    #     return output
 
     async def process_conversation(self, update: Update, context: CallbackContext) -> int:
         message = update.message.text
-        instruction = f'Instruction: given a dialog context, you need to response empathically.'
-        knowledge = ''
-        dialog = []
-        dialog .append(message)
-        text = self.generate(instruction, knowledge, dialog)
+        # instruction = f'Instruction: given a dialog context, you need to response empathically.'
+        # knowledge = ''
+        # dialog = []
+        # dialog .append(message)
+        text = self.predict(message)
         await update.message.reply_text(f'{text}')

src/api.py

@@ -4,7 +4,7 @@ from typing import List, Optional, Type
 from pydantic import Field
 
 
-from agent.base import LangChainAgentBot 
+from agent.base import LangChainAgentBot
 from telegram.ext import Updater, CommandHandler, CallbackContext, Application, ContextTypes
 from telegram import Update
 from telegram.ext import (
@@ -20,14 +20,10 @@ VERBOSE = True
 
 
 async def hello(update: Update, context: CallbackContext) -> None:
-    intro_text = f"""
-    🤖 Welcome to BearBuddy, created by rexthecoder! I'm your extraordinary AI companion capable of accomplishing the impossible!
-    💬 Feel free to ask me about anything, whether it's mouthwatering 🍔 recipes, exciting ✈️ travel destinations, effective 🏋️‍♀️ fitness routines, strategic 📱 marketing tips, or any other topic you can think of. 
-    Don't worry about the language barrier—I'm here to assist you in any language!
-    How can I assist you today?
-    """
+    intro_text = "🤖 Welcome to BearBuddy, crafted by rexthecoder! I'm your extraordinary AI companion capable of accomplishing the impossible!\n\n💬 Feel free to ask me about anything, whether it's mouthwatering 🍔 recipes, exciting ✈️ travel destinations, effective 🏋️‍♀️ fitness routines, strategic 📱 marketing tips, or any other topic you can think of.\n\nDon't worry about the language barrier—I'm here to assist you in any language!\nHow can I assist you today?"
     await update.message.reply_text(intro_text)
 
+
 class GirlfriendGPT(LangChainAgentBot, ):
     """Deploy LangChain chatbots and connect them to Telegram."""
 
@@ -41,10 +37,11 @@ class GirlfriendGPT(LangChainAgentBot, ):
         # Run the bot until the user presses Ctrl-C
         # self.application.run_polling()
         self.token = token
-    
+
     def handlers(self):
         summary_handler = self.conversation_summary_handler()
-        self.application.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, self.process_conversation))
+        self.application.add_handler(MessageHandler(
+            filters.TEXT & ~filters.COMMAND, self.process_conversation))
         self.application.add_handler(summary_handler)
         self.application.add_handler(CommandHandler('start', hello))