Update app.py

app.py

@@ -1,22 +1,50 @@
-from dotenv import load_dotenv
-from langchain import HuggingFaceHub, LLMChain
-from langchain import PromptTemplates
-import gradio
+# from dotenv import load_dotenv
+# from langchain import HuggingFaceHub, LLMChain
+# from langchain import PromptTemplates
+# import gradio
 
-load_dotenv()
-os.getenv('HF_API')
+# load_dotenv()
+# os.getenv('HF_API')
 
-hub_llm = HuggingFaceHub(repo_id='facebook/blenderbot-400M-distill')
+# hub_llm = HuggingFaceHub(repo_id='facebook/blenderbot-400M-distill')
 
-prompt = prompt_templates(
-    input_variable = ["question"],
-    template = "Answer is: {question}"
-)
+# prompt = prompt_templates(
+#     input_variable = ["question"],
+#     template = "Answer is: {question}"
+# )
+
+# hub_chain = LLMChain(prompt=prompt, llm=hub_llm, verbose=True)
+
+
+
+
+
+# Sample code for AI language model interaction
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import gradio
+
+
+def simptok(data):
+    # Load pre-trained model and tokenizer (using the transformers library)
+    model_name = "gpt2"
+    tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+    model = GPT2LMHeadModel.from_pretrained(model_name)
+    
+    # User input
+    user_input = data
+    
+    # Tokenize input
+    input_ids = tokenizer.encode(user_input, return_tensors="pt")
+    
+    # Generate response
+    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
+    response = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    print(response)
 
-hub_chain = LLMChain(prompt=prompt, llm=hub_llm, verbose=True)
 
 def responsenew(data):
-    return hub_chain(data)
+    return simptok(data)
 
 
 gradio_interface = gradio.Interface(