Update app.py

app.py

@@ -59,6 +59,9 @@ I am confident that I can leverage my expertise to assist you in developing and
 
         return summary, next_step
 
+def load_hf_token():
+    return 'YOUR_HF_TOKEN'
+
 def save_agent_to_file(agent):
     """Saves the agent's prompt to a file."""
     if not os.path.exists(AGENT_DIRECTORY):
@@ -91,8 +94,8 @@ def chat_interface_with_agent(input_text, agent_name):
 
     model_name = "Bin12345/AutoCoder_S_6.7B"
     try:
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name, use_auth_token=load_hf_token())
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=load_hf_token())
         generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
     except EnvironmentError as e:
         return f"Error loading model: {e}"
@@ -143,33 +146,33 @@ def code_editor_interface(code):
 
 # Text summarization tool
 def summarize_text(text):
-    summarizer = pipeline("summarization")
+    summarizer = pipeline("summarization", model="t5-base", use_auth_token=load_hf_token())
     summary = summarizer(text, max_length=130, min_length=30, do_sample=False)
     return summary[0]['summary_text']
 
 # Sentiment analysis tool
 def sentiment_analysis(text):
-    analyzer = pipeline("sentiment-analysis")
+    analyzer = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment", use_auth_token=load_hf_token())
     result = analyzer(text)
     return result[0]['label']
 
 # Text translation tool (code translation)
 def translate_code(code, source_language, target_language):
     # Use a Hugging Face translation model instead of OpenAI
-    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es")  # Example: English to Spanish
+    translator = pipeline("translation", model="Helsinki-NLP/opus-mt-en-es", use_auth_token=load_hf_token())  # Example: English to Spanish
     translated_code = translator(code, target_lang=target_language)[0]['translation_text']
     return translated_code
 
 def generate_code(code_idea):
     # Use a Hugging Face code generation model instead of OpenAI
-    generator = pipeline('text-generation', model='bigcode/starcoder')
+    generator = pipeline('text-generation', model='bigcode/starcoder', use_auth_token=load_hf_token())
     generated_code = generator(code_idea, max_length=1000, num_return_sequences=1)[0]['generated_text']
     return generated_code
 
 def chat_interface(input_text):
     """Handles general chat interactions with the user."""
     # Use a Hugging Face chatbot model or your own logic
-    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+    chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium", use_auth_token=load_hf_token())
     response = chatbot(input_text, max_length=50, num_return_sequences=1)[0]['generated_text']
     return response