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Update app.py
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app.py
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@@ -1,19 +1,20 @@
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse, JSONResponse
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from fastapi.staticfiles import StaticFiles
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from pathlib import Path
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import os
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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#
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cache_dir = os.getenv("TRANSFORMERS_CACHE", "/cache")
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os.makedirs(cache_dir, exist_ok=True)
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#
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hf_token = os.getenv("HF_TOKEN")
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# Load model
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model_id = "mistralai/Mistral-7B-Instruct-v0.2"
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, cache_dir=cache_dir)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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@@ -23,17 +24,26 @@ model = AutoModelForCausalLM.from_pretrained(
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torch_dtype="auto"
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)
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#
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app = FastAPI()
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# Serve static files
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app.mount("/static", StaticFiles(directory="."), name="static")
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# Route: Serve index.html at root
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@app.get("/", response_class=HTMLResponse)
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async def
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html_path = Path("index.html")
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return HTMLResponse(content=html_path.read_text(), status_code=200)
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return JSONResponse(content={"answer": "❗ Please enter a valid question."})
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prompt = f"[INST] {question} [/INST]"
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import os
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from pathlib import Path
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse, JSONResponse, FileResponse
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from fastapi.staticfiles import StaticFiles
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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# Setup Hugging Face cache directory
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cache_dir = os.getenv("TRANSFORMERS_CACHE", "/cache")
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os.makedirs(cache_dir, exist_ok=True)
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# Optional token (for private models)
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hf_token = os.getenv("HF_TOKEN")
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# Load model and tokenizer
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model_id = "mistralai/Mistral-7B-Instruct-v0.2"
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tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, cache_dir=cache_dir)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto"
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)
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# Build generation pipeline
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pipe = pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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max_new_tokens=256,
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temperature=0.7,
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top_p=0.9,
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repetition_penalty=1.1
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)
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# Initialize FastAPI app
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app = FastAPI()
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# Serve static files like script.js
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app.mount("/static", StaticFiles(directory="."), name="static")
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# Route: Serve index.html at root
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@app.get("/", response_class=HTMLResponse)
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async def serve_home():
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html_path = Path("index.html")
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return HTMLResponse(content=html_path.read_text(), status_code=200)
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return JSONResponse(content={"answer": "❗ Please enter a valid question."})
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prompt = f"[INST] {question} [/INST]"
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try:
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output = pipe(prompt)[0]["generated_text"]
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return JSONResponse(content={"answer": output.strip()})
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except Exception as e:
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return JSONResponse(content={"answer": f"⚠️ Error: {str(e)}"})
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# Optional: Serve script.js if not using /static path in HTML
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@app.get("/script.js")
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async def serve_script():
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return FileResponse("script.js", media_type="application/javascript")
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