Update app.py

app.py

@@ -1,70 +1,83 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+import os, torch, gradio as gr, spaces
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
 
+MODEL_ID = os.getenv("MODEL_ID", "JDhruv14/Gita-FT-v2-Qwen2.5-3B")
 
-def respond(
-    message,
-    history: list[dict[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-    hf_token: gr.OAuthToken,
-):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
-    client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
-
-    messages = [{"role": "system", "content": system_message}]
-
-    messages.extend(history)
-
-    messages.append({"role": "user", "content": message})
+# Load once (CPU until first call; device_map will move to GPU on first run)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    torch_dtype=torch.bfloat16 if torch.cuda.is_available() else "auto",
+    trust_remote_code=True,
+)
 
-    response = ""
+def _msgs_from_history(history, system_text):
+    msgs = []
+    if system_text:
+        msgs.append({"role": "system", "content": system_text})
+    for user, assistant in history:
+        if user:
+            msgs.append({"role": "user", "content": user})
+        if assistant:
+            msgs.append({"role": "assistant", "content": assistant})
+    return msgs
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        choices = message.choices
-        token = ""
-        if len(choices) and choices[0].delta.content:
-            token = choices[0].delta.content
+def _eos_ids(tok):
+    ids = {tok.eos_token_id}
+    im_end = tok.convert_tokens_to_ids("<|im_end|>")
+    if im_end is not None:
+        ids.add(im_end)
+    return list(ids)
 
-        response += token
-        yield response
+@spaces.GPU(duration=120)  # REQUIRED for ZeroGPU; remove if using standard GPU hardware
+def chat_fn(message, history, system_text, temperature, top_p, max_new, min_new):
+    msgs = _msgs_from_history(history, system_text) + [{"role": "user", "content": message}]
+    prompt = tokenizer.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer([prompt], return_tensors="pt").to(model.device)
 
+    gen_cfg = GenerationConfig(
+        do_sample=True,
+        temperature=float(temperature),
+        top_p=float(top_p),
+        max_new_tokens=int(max_new),
+        min_new_tokens=int(min_new),
+        repetition_penalty=1.02,
+        no_repeat_ngram_size=3,
+        eos_token_id=_eos_ids(tokenizer),
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    with torch.no_grad():
+        out = model.generate(**inputs, generation_config=gen_cfg)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-chatbot = gr.ChatInterface(
-    respond,
-    type="messages",
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+    # slice off the prompt so we show only the assistant reply
+    new_tokens = out[:, inputs["input_ids"].shape[1]:]
+    reply = tokenizer.batch_decode(new_tokens, skip_special_tokens=True)[0].strip()
+    return reply
 
 with gr.Blocks() as demo:
-    with gr.Sidebar():
-        gr.LoginButton()
-    chatbot.render()
+    gr.Markdown(
+        "<h1 style='text-align:center'>Gita Assistant (Qwen2.5-3B Fine-tuned)</h1>"
+        "<p style='text-align:center'>Ask in English / हिंदी / ગુજરાતી. The assistant cites verses when relevant.</p>"
+    )
+    system_box = gr.Textbox(
+        value="Reply in the user’s language with 2–3 concise points (200–400 words); cite Gita verses when relevant.",
+        label="System prompt",
+    )
+    temperature = gr.Slider(0.1, 1.2, value=0.7, step=0.05, label="temperature")
+    top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="top_p")
+    max_new = gr.Slider(64, 1024, value=512, step=16, label="max_new_tokens")
+    min_new = gr.Slider(0, 512, value=160, step=8, label="min_new_tokens")
 
+    chat = gr.ChatInterface(
+        fn=lambda m, h: chat_fn(m, h, system_box.value, temperature.value, top_p.value, max_new.value, min_new.value),
+        title=None,
+        additional_inputs=[system_box, temperature, top_p, max_new, min_new],
+        retry_btn="Regenerate",
+        undo_btn="Undo Last",
+        clear_btn="Clear",
+        queue=True,  # queue is recommended (and required for ZeroGPU concurrency)
+    )
 
 if __name__ == "__main__":
     demo.launch()