Update app.py

app.py

@@ -13,53 +13,43 @@ def generate_text(user_prompt):
   """Generates text using the BLOOM model from Hugging Face Transformers and removes the user prompt."""
   # Construct the full prompt with system introduction, user prompt, and assistant role
   prompt = f"<|system|> You are a helpful AI assistant. </s> <|prompter|> {user_prompt} </s> <|assistant|>"
-  encoded_prompt = tokenizer(prompt, return_tensors="pt").input_ids
 
-  # Initialize variables for real-time generation
-  generated_text = ""
-  current_output = torch.tensor([tokenizer.encode("<|assistant|>", return_tensors="pt").input_ids[0]])
-
-  for char in user_prompt:
-    # Encode character and concatenate with previous output
-    encoded_char = torch.tensor([tokenizer.encode(char, return_tensors="pt").input_ids[0]])
-    input_ids = torch.cat((current_output, encoded_char), dim=-1)
-
-    # Generate text with the current prompt and encoded character
-    output = model.generate(
-        input_ids=input_ids,
-        max_length=256,
-        num_beams=1,
-        num_return_sequences=1,  
-        do_sample=True,  
-        top_k=50,  
-        top_p=0.95,  
-        temperature=0.2,  
-        repetition_penalty=1.155
-    )
-
-    # Decode the generated token sequence back to text
-    decoded_text = tokenizer.decode(output[0], skip_special_tokens=True)
-
-    # Extract and update generated text, removing special tokens
-    generated_text += decoded_text.split("<|assistant|>")[-1].strip()
-    current_output = input_ids
-
-  # Remove prompt and user input from final response
-  assistant_response = generated_text.replace(f"{user_prompt}", "").strip()
+  # Encode the entire prompt into tokens
+  prompt_encoded = tokenizer(prompt, return_tensors="pt").input_ids
+
+  # Generate text with the complete prompt and limit the maximum length to 256 tokens
+  output = model.generate(
+      input_ids=prompt_encoded,
+      max_length=256,
+      num_beams=1,
+      num_return_sequences=1,  
+      do_sample=True,  
+      top_k=50,  
+      top_p=0.95,  
+      temperature=0.2,  
+      repetition_penalty=1.155
+  )
+
+  # Decode the generated token sequence back to text
+  generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+
+  # Extract the assistant's response (assuming it starts with "<|assistant|>")
+  assistant_response = generated_text.split("<|assistant|>")[-1]
+  assistant_response = assistant_response.replace(f"{user_prompt}", "").strip()
   assistant_response = assistant_response.replace("You are a helpful AI assistant.", "").strip()
   
   return assistant_response
 
-# Define the Gradio interface with streaming enabled
+# Define the Gradio interface
 interface = gr.Interface(
   fn=generate_text,
   inputs=[
-      gr.Textbox(label="Text Prompt", value="", type="verbatim"),  # Set type to "verbatim" for character-by-character input
+      gr.Textbox(label="Text Prompt", value="What's an AI?"),
   ],
   outputs="text",
   description="Interact with BLOOM-3b-conversational (Loaded with Hugging Face Transformers)",
-  **{"allow_user_code": True},  # Enable user code execution for real-time updates
 )
 
+
 # Launch the Gradio interface
-interface.launch()
+interface.launch()