Update README.md

README.md

@@ -2,7 +2,6 @@ import atexit
 import json
 import os
 import sys
-from collections.abc import Generator
 
 import nltk
 import numpy as np
@@ -10,31 +9,29 @@ import pyttsx3
 import spacy
 import spacy as nlp
 import speech_recognition as sr
+import torch
 import transformers
 from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer
 from sklearn.feature_extraction.text import TfidfVectorizer
-from transformers import AutoTokenizer
-from transformers import GPTNeoForCausalLM
-from transformers import pipeline
-
+from transformers import AutoTokenizer, GPTNeoForCausalLM, pipeline, AutoModelForCausalLM
 from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 
-import torch
-print(torch.__version__)
-
 
 
-model_path = spacy.util.get_package_path('en_core_web_sm')
-print(model_path)
 
 print("transformers version:", transformers.__version__)
 print("spacy version:", spacy.__version__)
 print("nltk version:", nltk.__version__)
 
+from transformers import AutoTokenizer
+
+vocab_file = r"C:/Users/withe/PycharmProjects/no hope2/EleutherAI2/vocab.txt"
+tokenizer = AutoTokenizer.from_pretrained("razerblade072611/EleutherAI2", vocab_file=vocab_file)
 
+merges_file = r"C:/Users/withe/PycharmProjects/no hope2/EleutherAI2/merges.txt"
+tokenizer = AutoTokenizer.from_pretrained("razerblade072611/EleutherAI2", merges_file=merges_file)
 
-sys.path.append(r"C:\Users\withe\PycharmProjects\no hope2\Gpt-Neo1")
 
 # Download necessary NLTK resources
 nltk.download('punkt')
@@ -42,7 +39,6 @@ nltk.download('stopwords')
 nltk.download('wordnet')
 nltk.download('omw-1.4')
 
-
 # Load the spaCy model
 nlp = spacy.load('en_core_web_sm')
 
@@ -52,7 +48,6 @@ text = "Example text to process"
 # Process the text using the nlp object
 doc = nlp(text)
 
-
 # Extract named entities from the processed text
 named_entities = []
 for entity in doc.ents:
@@ -67,7 +62,7 @@ dotenv_path = './API_KEY.env'
 (dotenv_path)
 
 # Check if GPU is available and set the device accordingly
-device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
 
 # Initialize the speech engine
 speech_engine = pyttsx3.init()
@@ -109,9 +104,9 @@ class CommonModule:
         self.name = name
         self.param1 = param1
         self.param2 = param2
-        self.tokenizer = AutoTokenizer.from_pretrained(model)  # Load the tokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained("razerblade072611/EleutherAI2")  # Load the tokenizer
         self.tokenizer.add_special_tokens({'pad_token': '[PAD]'})
-        self.gpt3_model = GPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-1.3B')
+        self.gpt3_model = AutoModelForCausalLM.from_pretrained("razerblade072611/EleutherAI2")
         self.gpt3_model.to(device)  # Move model to the device (GPU or CPU)
         self.memory_module = MemoryModule()
         self.sentiment_module = SentimentAnalysisModule()
@@ -123,8 +118,6 @@ class CommonModule:
         self.temperature = 0.3
         self.response_cache = {}  # Cache for storing frequently occurring responses
 
-
-
     def reset_conversation(self):
         self.memory_module.reset_memory()
 
@@ -135,18 +128,17 @@ class CommonModule:
                 return self.response_cache.get(entity)
         return None
 
-
-
     def generate_gpt3_response(self, input_text, conversation_history, temperature=0.3):
         prompt = '\n'.join(conversation_history) + '\n' + input_text + '\n'
 
-        generator = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B')
+        generator = pipeline('text-generation', model="razerblade072611/EleutherAI2")
         output = generator(
             prompt,
             do_sample=True,
-            min_length=50,
-            temperature=temperature,
-            num_return_sequences=1
+            min_length=10,
+            max_length=50,
+            num_return_sequences=2,
+            temperature=0.3
         )
 
         if output:
@@ -332,7 +324,7 @@ def exit_handler(common_module):
         'memory': common_module.memory_module.memory,
         'named_entities': list(common_module.memory_module.named_entities)
     }
-    common_module.save_memory('C:\\Users\\withe\\PycharmProjects\\pythonProject2\\Chat_bot1\\save_memory.json')
+    common_module.save_memory('C:\\Users\\withe\\PycharmProjects\\no hope2\\Chat_Bot1\\save_memory.json')
     print("Memory data saved successfully.")
 
     return memory_data
@@ -343,29 +335,22 @@ def check_memory_file(file_path):
     return os.path.isfile(file_path)
 
 
+# Modify the main section of the code to load memory data if the file exists
 if __name__ == "__main__":
-    model = 'EleutherAI/gpt-neo-1.3B'
+    model = '"EleutherAI2"'
     name = "Chat bot1"
     param1 = 'value1'
     param2 = 'value2'
     common_module = CommonModule(model, name, param1, param2)
 
-    memory_file_path = 'C:\\Users\\withe\\PycharmProjects\\pythonProject2\\Chat_bot1\\load_memory1.json'
+    memory_file_path = 'C:\\Users\\withe\\PycharmProjects\\no hope2\\Chat_Bot1\\load_memory1.json'
     if check_memory_file(memory_file_path):
         with open(memory_file_path, 'r') as file:
             memory_data = json.load(file)
         common_module.load_memory_data(memory_data)
 
+    # Register the exit handler
     atexit.register(exit_handler, common_module)
 
     common_module.converse()
-
-    # Generate response using the generator
-    prompt = "EleutherAI has"
-    generated_text = Generator(prompt, do_sample=True, min_length=50)
-
-    if generated_text:
-        generated_response = generated_text[0]['generated_text'].strip()
-        print(generated_response)
-
-    common_module.save_memory(memory_file_path)
+    common_module.save_memory(memory_file_path)