Create app.py

app.py

@@ -0,0 +1,73 @@
+import torch
+from fastapi import FastAPI, Request, Form
+from fastapi.responses import HTMLResponse, JSONResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# ---------------------------
+# Models
+# ---------------------------
+MODEL_OPTIONS = {
+    "DeepSeek Coder 1.3B": "deepseek-ai/deepseek-coder-1.3b-instruct",
+    "StarCoder 1B": "bigcode/starcoderbase-1b",
+    "CodeLLaMA 7B": "codellama/CodeLlama-7b-Instruct-hf"
+}
+loaded_models = {}
+
+def get_model(model_key):
+    if model_key not in loaded_models:
+        model_name = MODEL_OPTIONS[model_key]
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            device_map="auto"
+        )
+        loaded_models[model_key] = (tokenizer, model)
+    return loaded_models[model_key]
+
+# ---------------------------
+# FastAPI setup
+# ---------------------------
+app = FastAPI()
+app.mount("/static", StaticFiles(directory="static"), name="static")
+templates = Jinja2Templates(directory=".")
+
+# ---------------------------
+# Routes
+# ---------------------------
+@app.get("/", response_class=HTMLResponse)
+async def home(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request, "models": list(MODEL_OPTIONS.keys())})
+
+@app.post("/chat")
+async def chat(user_input: str = Form(...), model_choice: str = Form(...), history: str = Form("[]")):
+    import json
+    history = json.loads(history)
+
+    tokenizer, model = get_model(model_choice)
+
+    # Build messages
+    messages = []
+    for role, content in history:
+        messages.append({"role": "user" if role == "user" else "assistant", "content": content})
+    messages.append({"role": "user", "content": user_input})
+
+    # Tokenize
+    inputs = tokenizer.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_dict=True,
+        return_tensors="pt"
+    ).to(model.device)
+
+    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
+    response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+
+    # Update history
+    history.append(("user", user_input))
+    history.append(("assistant", response))
+
+    return JSONResponse({"response": response, "history": history})