Create app.py

app.py

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+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+app = FastAPI()
+
+# === MODEL ===
+MODEL_REPO = "sahil239/falcon-lora-chatbot"  # replace with your HF repo
+BASE_MODEL = "tiiuae/falcon-rw-1b"
+
+# === Load tokenizer ===
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token  # required to avoid padding error
+
+# === Load base model and merge LoRA ===
+base_model = AutoModelForCausalLM.from_pretrained(BASE_MODEL, trust_remote_code=True)
+model = PeftModel.from_pretrained(base_model, MODEL_REPO)
+model.eval()
+
+# === Move to GPU if available ===
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# === Request Schema ===
+class PromptRequest(BaseModel):
+    prompt: str
+    max_new_tokens: int = 200
+    temperature: float = 0.7
+    top_p: float = 0.95
+
+@app.get("/")
+def health_check():
+    return {"status": "running"}
+
+@app.post("/generate")
+async def generate_text(req: PromptRequest):
+    inputs = tokenizer(
+        req.prompt,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=200
+    )
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=inputs["input_ids"],
+            attention_mask=inputs["attention_mask"],
+            max_new_tokens=req.max_new_tokens,
+            temperature=req.temperature,
+            top_p=req.top_p,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            eos_token_id=tokenizer.eos_token_id,  # 🚨 Helps stop when sentence is "done"
+            repetition_penalty=1.2,  # 🚫 Penalizes repeating phrases
+            no_repeat_ngram_size=3
+        )
+
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return {"response": generated_text[len(req.prompt):].strip()}