Create app.py

app.py

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+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import torch
+
+# Initialize FastAPI app
+app = FastAPI()
+
+# Load the Falcon-7B model with 8-bit quantization (if CUDA is available)
+model_id = "tiiuae/falcon-7b-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Check if CUDA is available
+if torch.cuda.is_available():
+    # Load the model with 8-bit quantization for GPU
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        load_in_8bit=True,
+        device_map="auto",
+        trust_remote_code=True
+    )
+else:
+    # Fallback to CPU or full precision
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        device_map="auto",
+        trust_remote_code=True
+    )
+
+# Create a text generation pipeline
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+# Define request body schema
+class TextGenerationRequest(BaseModel):
+    prompt: str
+    max_new_tokens: int = 50
+    temperature: float = 0.7
+    top_k: int = 50
+    top_p: float = 0.9
+    do_sample: bool = True
+
+# Define API endpoint
+@app.post("/generate-text")
+async def generate_text(request: TextGenerationRequest):
+    try:
+        # Generate text using the pipeline
+        outputs = pipe(
+            request.prompt,
+            max_new_tokens=request.max_new_tokens,
+            temperature=request.temperature,
+            top_k=request.top_k,
+            top_p=request.top_p,
+            do_sample=request.do_sample
+        )
+        return {"generated_text": outputs[0]["generated_text"]}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Add a root endpoint for health checks
+@app.get("/test")
+async def root():
+    return {"message": "API is running!"}