Update app.py

app.py

@@ -2,26 +2,22 @@ from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import StreamingResponse, JSONResponse
-import pandas as pd
-import os
-import requests
-from io import StringIO
 from fastapi.middleware.cors import CORSMiddleware
-from huggingface_hub import HfFolder
-from tqdm import tqdm
+import logging
 
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
 
 app = FastAPI()
 
 # Enable CORS
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  
+    allow_origins=["*"],  # Allow all origins (replace with your frontend URL in production)
     allow_credentials=True,
-    allow_methods=["*"],  # Allow all methods
-    allow_headers=["*"],  # Allow all headers
+    allow_methods=["*"],
+    allow_headers=["*"],
 )
 
 # Load your fine-tuned model and tokenizer
@@ -31,6 +27,7 @@ try:
     tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
     model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
 except Exception as e:
+    logger.error(f"Failed to load model or tokenizer: {str(e)}")
     raise RuntimeError(f"Failed to load model or tokenizer: {str(e)}")
 
 # Define the general prompt template
@@ -71,12 +68,16 @@ def generate_text(request: GenerateRequest):
     المادة = request.المادة
     المستوى = request.المستوى
 
+    logger.info(f"Received request: المادة={المادة}, المستوى={المستوى}")
+
     if not المادة or not المستوى or not isinstance(المادة, str) or not isinstance(المستوى, str):
+        logger.error("المادة والمستوى مطلوبان ويجب أن يكونا نصًا.")
         raise HTTPException(status_code=400, detail="المادة والمستوى مطلوبان ويجب أن يكونا نصًا.")
 
     try:
         # Format the prompt with user inputs
         arabic_prompt = general_prompt_template.format(المادة=المادة, المستوى=المستوى)
+        logger.info(f"Formatted prompt: {arabic_prompt}")
 
         # Tokenize the prompt
         inputs = tokenizer(arabic_prompt, return_tensors="pt", max_length=512, truncation=True)
@@ -94,6 +95,7 @@ def generate_text(request: GenerateRequest):
 
         # Decode the generated text
         generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        logger.info(f"Generated text: {generated_text}")
 
         # Remove the prompt from the generated text
         generated_text = generated_text.replace(arabic_prompt, "").strip()
@@ -101,6 +103,7 @@ def generate_text(request: GenerateRequest):
         return {"generated_text": generated_text}
 
     except Exception as e:
+        logger.error(f"Error during text generation: {str(e)}")
         raise HTTPException(status_code=500, detail=f"Error during text generation: {str(e)}")
 
 @app.get("/")