Create app.py

app.py

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+"""
+BharatGen AyurParam — Swastik.fit AI Vaidya
+Hosted on HuggingFace Spaces with ZeroGPU (free, no credit card needed)
+
+Model: bharatgenai/AyurParam (2.9B params, trained on 1,000+ Ayurvedic texts)
+License: CC-BY-4.0 (commercial OK)
+Prompt format: <user> {question} <assistant>
+
+This Space is called by the Swastik Cloud Function (ayurParamProxy).
+The /run/predict endpoint receives: { data: ["<user> ... <assistant>"] }
+Returns: { data: ["response text"] }
+"""
+
+import spaces
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+MODEL_ID = "bharatgenai/AyurParam"
+
+tokenizer = None
+model = None
+
+
+def load_model():
+    global tokenizer, model
+    if model is not None:
+        return
+    print("[AyurParam] Loading tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=False)
+    print("[AyurParam] Loading model...")
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        trust_remote_code=True,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+    model.eval()
+    print("[AyurParam] Model ready.")
+
+
+# Load on startup
+load_model()
+
+
+@spaces.GPU
+def generate(prompt: str) -> str:
+    """
+    Main inference function.
+    Accepts either:
+      - Raw prompt already formatted: "<user> ... <assistant>"
+      - Plain text question (will be wrapped automatically)
+    Returns: assistant response only (no prompt echo)
+    """
+    if not prompt or not prompt.strip():
+        return "Please provide a question."
+
+    # Ensure correct prompt format
+    if "<user>" not in prompt:
+        formatted = f"<user> {prompt.strip()} <assistant>"
+    else:
+        # Already formatted — ensure it ends with <assistant>
+        formatted = prompt.strip()
+        if not formatted.endswith("<assistant>"):
+            formatted = formatted + " <assistant>"
+
+    inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
+    input_len = inputs["input_ids"].shape[1]
+
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=512,
+            do_sample=True,
+            top_k=50,
+            top_p=0.95,
+            temperature=0.6,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.eos_token_id,
+            use_cache=True,
+        )
+
+    # Decode only the new tokens (not the prompt)
+    new_tokens = output[0][input_len:]
+    response = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
+
+    # Clean up any trailing special tokens
+    for stop in ["<user>", "<context>", "</s>"]:
+        if stop in response:
+            response = response[: response.index(stop)].strip()
+
+    return response
+
+
+# Gradio interface — Swastik Cloud Function calls /run/predict directly
+demo = gr.Interface(
+    fn=generate,
+    inputs=gr.Textbox(
+        label="Prompt",
+        placeholder="<user> What foods should I eat for better digestion? <assistant>",
+        lines=3,
+    ),
+    outputs=gr.Textbox(label="AyurParam Response", lines=8),
+    title="BharatGen AyurParam — Ayurveda AI",
+    description=(
+        "**AyurParam** is India's first AI trained on 1,000+ Ayurvedic texts (54.5M words). "
+        "2.9B parameter model fine-tuned on classical Ayurveda knowledge.\n\n"
+        "Prompt format: `<user> your question <assistant>`\n\n"
+        "This Space powers the AI Vaidya at [swastik.fit](https://swastik.fit)."
+    ),
+    examples=[
+        ["<user> What foods should I eat to improve digestion according to Ayurveda? <assistant>"],
+        ["<user> I have vata imbalance — what daily routine do you recommend? <assistant>"],
+        ["<user> What are the benefits of turmeric in Ayurvedic medicine? <assistant>"],
+        ["<user> namaste <assistant>"],  # warmup ping
+    ],
+    cache_examples=False,
+    api_name="predict",  # enables /run/predict endpoint
+)
+
+if __name__ == "__main__":
+    demo.launch()