Update app.py

app.py

@@ -1,64 +1,72 @@
+from transformers import T5Tokenizer, T5ForConditionalGeneration
 import gradio as gr
-from huggingface_hub import InferenceClient
+import requests
+import re
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# تحميل نموذج CodeT5
+model_name = "Salesforce/codet5-base"
+tokenizer = T5Tokenizer.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
 
+# وظيفة لجلب الأكواد من الإنترنت تلقائيًا (مثلاً GitHub Gist أو مستودعات GitHub)
+def fetch_code_from_github(keyword, language="python"):
+    try:
+        # البحث عن الأكواد عبر GitHub API
+        url = f"https://api.github.com/search/code?q={keyword}+language:{language}"
+        headers = {"Accept": "application/vnd.github.v3+json"}
+        response = requests.get(url, headers=headers)
+        
+        if response.status_code == 200:
+            results = response.json()
+            # استخراج الروابط والمحتوى
+            items = results.get("items", [])
+            if items:
+                first_result = items[0]
+                file_url = first_result["html_url"]
+                return f"Code fetched from: {file_url}"
+            else:
+                return "No matching code found on GitHub."
+        else:
+            return f"Error fetching code: {response.status_code}"
+    except Exception as e:
+        return str(e)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# وظيفة للتعديل أو التوليد باستخدام CodeT5
+def modify_or_generate_code(input_text, task="generate_code"):
+    try:
+        inputs = tokenizer(input_text, return_tensors="pt", max_length=512, truncation=True)
+        outputs = model.generate(**inputs, max_length=512, num_beams=4, early_stopping=True)
+        generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return generated_code
+    except Exception as e:
+        return str(e)
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# واجهة Gradio
+def main_interface(task, input_text, fetch_keyword=None, language="python"):
+    if task == "fetch_code":
+        return fetch_code_from_github(fetch_keyword, language)
+    elif task == "generate_code":
+        return modify_or_generate_code(input_text, task)
+    else:
+        return "Invalid task selected."
 
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# إنشاء واجهة Gradio
+interface = gr.Interface(
+    fn=main_interface,
+    inputs=[
+        gr.Radio(["fetch_code", "generate_code"], label="Task"),
+        gr.Textbox(lines=5, placeholder="Enter your input text or description here...", label="Input Text"),
+        gr.Textbox(placeholder="Enter keyword for fetching code...", label="Fetch Keyword (Optional)"),
+        gr.Textbox(value="python", placeholder="Language (default: python)", label="Programming Language"),
     ],
+    outputs="text",
+    title="CodeT5 Code Assistant",
+    description="Generate or fetch code snippets using CodeT5 with extended features.",
+    examples=[
+        ["generate_code", "Translate Python to Java: def add(a, b): return a + b", None, "python"],
+        ["fetch_code", None, "factorial", "python"]
+    ]
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    interface.launch()