Initial commit

app.py

@@ -2,40 +2,32 @@ import os
 import logging
 import sqlite3
 from datetime import datetime
-import torch
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
-from huggingface_hub import login
+from openai import OpenAI
 
 # تنظیم logging
 logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
-# تنظیم توکن
+# تنظیم API Key و مدل
 logger.info("Starting application setup")
-hf_token = os.getenv("HF_TOKEN")
-if not hf_token:
-    logger.error("HF_TOKEN not set")
-    raise ValueError("HF_TOKEN is required. Set it in Spaces Secrets (Settings > Repository secrets > Add secret > Name: HF_TOKEN).")
-login(token=hf_token)
-logger.info("Hugging Face login successful")
+qwen_api_key = os.getenv("QWEN_API_KEY")
+qwen_model = os.getenv("QWEN_MODEL", "qwen3-coder-plus")  # پیش‌فرض: qwen3-coder-plus
+if not qwen_api_key:
+    logger.error("QWEN_API_KEY not set")
+    raise ValueError("QWEN_API_KEY is required. Set it in Spaces Secrets (Settings > Repository secrets > Add secret > Name: QWEN_API_KEY).")
 
-# لود مدل GPT-Neo-1.3B
-logger.info("Loading EleutherAI/gpt-neo-1.3B model")
+# تنظیم کلاینت OpenAI برای DashScope
 try:
-    tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B", cache_dir="/tmp/hf_cache")
-    model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B", cache_dir="/tmp/hf_cache", torch_dtype=torch.float32, device_map="auto")
-    code_gen = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, temperature=0.6, top_p=0.95)
-    logger.info("EleutherAI/gpt-neo-1.3B model loaded")
+    client = OpenAI(
+        api_key=qwen_api_key,
+        base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1"
+    )
+    logger.info(f"Initialized OpenAI client for model {qwen_model}")
 except Exception as e:
-    logger.error(f"Failed to load GPT-Neo model: {e}")
+    logger.error(f"Failed to initialize OpenAI client: {e}")
     raise
 
-# آزادسازی حافظه
-import gc
-gc.collect()
-torch.cuda.empty_cache()
-
 # مسیر فایل دیتابیس
 DB_PATH = "chris.db"
 
@@ -103,10 +95,20 @@ def generate_code(text, language):
     try:
         prompt = f"Write a complete, correct, and well-explained code in {language} to: {text}"
         logger.info(f"Generated prompt: {prompt}")
-        result = code_gen(prompt, max_new_tokens=512, temperature=0.6, top_p=0.95, do_sample=True)[0]['generated_text']
+        response = client.chat.completions.create(
+            model=qwen_model,
+            messages=[
+                {"role": "system", "content": "You are a coding assistant that generates complete and well-explained code."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=1024,
+            temperature=0.7,
+            top_p=0.8
+        )
+        code_output = response.choices[0].message.content.strip()
         
-        # استخراج کد از خروجی
-        lines = result.split('\n')
+        # استخراج کد از خروجی (در صورت وجود بلاک کد)
+        lines = code_output.split('\n')
         code = []
         is_code = False
         for line in lines:
@@ -116,14 +118,13 @@ def generate_code(text, language):
             elif is_code:
                 code.append(line)
         
-        code_output = "\n".join(code).strip()
-        if not code_output:
+        final_code = "\n".join(code).strip() if code else code_output
+        if not final_code:
             logger.warning("Generated code is empty")
-            code_output = "خطا: کد تولیدشده خالی است"
+            final_code = "خطا: کد تولیدشده خالی است"
         
-        logger.info(f"Generated code: {code_output}")
-        torch.cuda.empty_cache()
-        return prompt, code_output
+        logger.info(f"Generated code: {final_code}")
+        return prompt, final_code
     except Exception as e:
         logger.error(f"Code generation error: {e}")
         return f"Error prompt: {text}", f"Code generation error: {e}"

requirements.txt

@@ -10,3 +10,4 @@ numpy
 transformers
 accelerate
 gradio
+openai