Update app.py

app.py

@@ -1,109 +1,112 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
-import torch
-import threading
+from huggingface_hub import InferenceClient
 import time
 
-model_id = "lambdaindie/lambdai"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model.to(device)
+client = InferenceClient("lambdaindie/lambdai")
 
 css = """
 @import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono&display=swap');
-* { font-family: 'JetBrains Mono', monospace !important; }
-body { background-color: #111; color: #e0e0e0; }
+
+{
+font-family: 'JetBrains Mono', monospace !important;
+}
+
+
+body {
+background-color: #111;
+color: #e0e0e0;
+}
+
 .markdown-think {
-    background-color: #1e1e1e;
-    border-left: 4px solid #555;
-    padding: 10px;
-    margin-bottom: 8px;
-    font-style: italic;
-    white-space: pre-wrap;
-    animation: pulse 1.5s infinite ease-in-out;
+background-color: #1e1e1e;
+border-left: 4px solid #555;
+padding: 10px;
+margin-bottom: 8px;
+font-style: italic;
+white-space: pre-wrap;
+animation: pulse 1.5s infinite ease-in-out;
 }
+
 @keyframes pulse {
-    0% { opacity: 0.6; }
-    50% { opacity: 1.0; }
-    100% { opacity: 0.6; }
+0% { opacity: 0.6; }
+50% { opacity: 1.0; }
+100% { opacity: 0.6; }
 }
 """
 
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    messages = [{"role": "system", "content": system_message}] if system_message else []
-
-    for user, assistant in history:
-        if user:
-            messages.append({"role": "user", "content": user})
-        if assistant:
-            messages.append({"role": "assistant", "content": assistant})
-
-    thinking_prompt = messages + [{"role": "user", "content": f"{message}\n\nThink step-by-step."}]
-    prompt = tokenizer.apply_chat_template(thinking_prompt, tokenize=False, add_generation_prompt=True)
-
-    inputs = tokenizer(prompt, return_tensors="pt").to(device)
-    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-
-    reasoning = ""
-    yield '<div class="markdown-think">Thinking...</div>'
-    start = time.time()
-
-    thread = threading.Thread(target=model.generate, kwargs={
-        "inputs": inputs["input_ids"],
-        "max_new_tokens": max_tokens,
-        "temperature": temperature,
-        "top_p": top_p,
-        "streamer": streamer,
-    })
-    thread.start()
-
-    for token in streamer:
-        reasoning += token
-        yield f'<div class="markdown-think">{reasoning.strip()}</div>'
-
-    elapsed = time.time() - start
-    yield f"""<div style="margin-top:12px;padding:8px 12px;background-color:#222;border-left:4px solid #888;
-              font-family:'JetBrains Mono', monospace;color:#ccc;font-size:14px;">
-              Pensou por {elapsed:.1f} segundos</div>"""
-
-    # Segunda etapa: resposta final
-    final_prompt = messages + [
-        {"role": "user", "content": message},
-        {"role": "assistant", "content": reasoning.strip()},
-        {"role": "user", "content": "Agora responda baseado nisso."}
-    ]
-    prompt2 = tokenizer.apply_chat_template(final_prompt, tokenize=False, add_generation_prompt=True)
-    inputs2 = tokenizer(prompt2, return_tensors="pt").to(device)
-    streamer2 = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
-
-    thread2 = threading.Thread(target=model.generate, kwargs={
-        "inputs": inputs2["input_ids"],
-        "max_new_tokens": max_tokens,
-        "temperature": temperature,
-        "top_p": top_p,
-        "streamer": streamer2,
-    })
-    thread2.start()
-
-    final_answer = ""
-    for token in streamer2:
-        final_answer += token
-        yield final_answer.strip()
+messages = [{"role": "system", "content": system_message}] if system_message else []
+
+for user, assistant in history:  
+    if user:  
+        messages.append({"role": "user", "content": user})  
+    if assistant:  
+        messages.append({"role": "assistant", "content": assistant})  
+
+thinking_prompt = messages + [{  
+    "role": "user",  
+    "content": f"{message}\n\nThink a bit step-by-step before answering."  
+}]  
+
+reasoning = ""  
+yield '<div class="markdown-think">Thinking...</div>'  
+
+start = time.time()  
+
+for chunk in client.chat_completion(  
+    thinking_prompt,  
+    max_tokens=max_tokens,  
+    stream=True,  
+    temperature=temperature,  
+    top_p=top_p,  
+):  
+    token = chunk.choices[0].delta.content or ""  
+    reasoning += token  
+    styled_thought = f'<div class="markdown-think">{reasoning.strip()}</div>'  
+    yield styled_thought  
+
+elapsed = time.time() - start  
+
+yield f"""  
+<div style="margin-top:12px;padding:8px 12px;background-color:#222;border-left:4px solid #888;  
+            font-family:'JetBrains Mono', monospace;color:#ccc;font-size:14px;">  
+    Pensou por {elapsed:.1f} segundos  
+</div>  
+"""  
+
+time.sleep(2)  
+
+final_prompt = messages + [  
+    {"role": "user", "content": message},  
+    {"role": "assistant", "content": reasoning.strip()},  
+    {"role": "user", "content": "Now answer based on your reasoning above."}  
+]  
+
+final_answer = ""  
+for chunk in client.chat_completion(  
+    final_prompt,  
+    max_tokens=max_tokens,  
+    stream=True,  
+    temperature=temperature,  
+    top_p=top_p,  
+):  
+    token = chunk.choices[0].delta.content or ""  
+    final_answer += token  
+    yield final_answer.strip()
 
 demo = gr.ChatInterface(
-    fn=respond,
-    title="λambdAI",
-    theme=gr.themes.Base(),
-    css=css,
-    additional_inputs=[
-        gr.Textbox(value="", label="System Message"),
-        gr.Slider(64, 2048, value=512, step=1, label="Max Tokens"),
-        gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
-    ]
+fn=respond,
+title="λambdAI",
+theme=gr.themes.Base(),
+css=css,
+additional_inputs=[
+gr.Textbox(value="",
+label="System Message"),
+gr.Slider(64, 2048, value=512, step=1, label="Max Tokens"),
+gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
+gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
+]
 )
 
-if __name__ == "__main__":
-    demo.launch()
+if name == "main":
+demo.launch()