Update app.py

app.py

@@ -24,9 +24,6 @@ from transformers import (
 from transformers.image_utils import load_image
 from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
-# -----------------------------
-# Existing global variables and model setup
-# -----------------------------
 MAX_MAX_NEW_TOKENS = 2048
 DEFAULT_MAX_NEW_TOKENS = 1024
 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
@@ -54,38 +51,6 @@ model_m = Qwen2VLForConditionalGeneration.from_pretrained(
     torch_dtype=torch.float16
 ).to("cuda").eval()
 
-# -----------------------------
-# New reasoning feature setup
-# -----------------------------
-from openai import OpenAI
-
-api_key = os.getenv("SAMBANOVA_API_KEY")
-client_reasoning = OpenAI(
-    base_url="https://api.sambanova.ai/v1/",
-    api_key=api_key,
-)
-
-def reasoning_predict(message, history):
-    """
-    This function appends the user's reasoning request to the history,
-    then streams the response from the Sambanova API using the model
-    'DeepSeek-R1-Distill-Llama-70B'.
-    """
-    history.append({"role": "user", "content": message})
-    stream = client_reasoning.chat.completions.create(
-        messages=history,
-        model="DeepSeek-R1-Distill-Llama-70B",
-        stream=True,
-    )
-    chunks = []
-    for chunk in stream:
-        # Accumulate streamed content and yield the current full response
-        chunks.append(chunk.choices[0].delta.content or "")
-        yield "".join(chunks)
-
-# -----------------------------
-# Utility Functions and Checks
-# -----------------------------
 async def text_to_speech(text: str, voice: str, output_file="output.mp3"):
     communicate = edge_tts.Communicate(text, voice)
     await communicate.save(output_file)
@@ -124,9 +89,6 @@ ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 
 dtype = torch.float16 if device.type == "cuda" else torch.float32
 
-# -----------------------------
-# Image Generation Models Setup
-# -----------------------------
 if torch.cuda.is_available():
     # Lightning 5 model
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -212,9 +174,6 @@ def save_image(img: Image.Image) -> str:
     img.save(unique_name)
     return unique_name
 
-# -----------------------------
-# Main Generation Function with Reasoning Integration
-# -----------------------------
 @spaces.GPU
 def generate(
     input_dict: dict,
@@ -229,7 +188,6 @@ def generate(
     files = input_dict.get("files", [])
 
     lower_text = text.lower().strip()
-
     # Check if the prompt is an image generation command using model flags.
     if (lower_text.startswith("@lightningv5") or 
         lower_text.startswith("@lightningv4") or 
@@ -282,20 +240,7 @@ def generate(
         yield gr.Image(image_path)
         return
 
-    # -----------------------------
-    # NEW: Reasoning Branch
-    # -----------------------------
-    if lower_text.startswith("@reasoning"):
-        reasoning_text = text.replace("@reasoning", "").strip()
-        reasoning_history = clean_chat_history(chat_history)
-        yield "Reasoning..."
-        for response in reasoning_predict(reasoning_text, reasoning_history):
-            yield response
-        return
-
-    # -----------------------------
     # Otherwise, handle text/chat (and TTS) generation.
-    # -----------------------------
     tts_prefix = "@tts"
     is_tts = any(text.strip().lower().startswith(f"{tts_prefix}{i}") for i in range(1, 3))
     voice_index = next((i for i in range(1, 3) if text.strip().lower().startswith(f"{tts_prefix}{i}")), None)
@@ -400,7 +345,6 @@ demo = gr.ChatInterface(
         ['@turbov3 "Abstract art, colorful and vibrant"'],
         ["Write a Python function to check if a number is prime."],
         ["@tts2 What causes rainbows to form?"],
-        ["@reasoning How does quantum entanglement work and what are its implications?"],
     ],
     cache_examples=False,
     type="messages",