Update app.py

app.py

@@ -1,20 +1,56 @@
-import os
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+import os
+import uuid
+
+app = FastAPI()
 
-model_id = "hugging-quants/Meta-Llama-3.1-8B-Instruct-GPTQ-INT4"
+model_id = "huggingface/your-quantized-model-id"
 hf_token = os.environ.get("HF_TOKEN")
 
 tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
-    device_map="auto",
     torch_dtype=torch.float16,
+    device_map="auto",
     low_cpu_mem_usage=True,
     token=hf_token
 )
 
-def generate(prompt):
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-    outputs = model.generate(**inputs, max_new_tokens=128)
-    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Store per-session system prompts
+session_prompts = {}
+
+class SystemPrompt(BaseModel):
+    prompt: str
+
+class UserMessage(BaseModel):
+    session_id: str
+    message: str
+
+@app.post("/start")
+def start_chat(system_prompt: SystemPrompt):
+    session_id = str(uuid.uuid4())
+    session_prompts[session_id] = system_prompt.prompt
+    return {"session_id": session_id}
+
+@app.post("/chat")
+def chat(message: UserMessage):
+    system = session_prompts.get(message.session_id)
+    if not system:
+        return {"error": "Invalid session_id. Call /start first."}
+
+    full_prompt = f"<|system|>\n{system}\n<|user|>\n{message.message}\n<|assistant|>\n"
+
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=200,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    # Strip input part to isolate model's answer
+    answer = response.replace(full_prompt.strip(), "").strip()
+    return {"response": answer}