update app.py

app.py

@@ -1,154 +1,48 @@
 import gradio as gr
-import numpy as np
-import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer, util
 import torch
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+# تحميل نموذج التضمين لتقييم المعنى
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+
+# تحميل النموذج اللغوي عبر pipeline
+# يمكن تغييره إلى LLaMA-2 أو أي نموذج متوافق مع HF
+generator = pipeline(
+    "text-generation", 
+    model="mistralai/Mistral-7B-Instruct-v0.1", 
+    tokenizer="mistralai/Mistral-7B-Instruct-v0.1",
+    device=0 if torch.cuda.is_available() else -1,
+    max_new_tokens=150,
+    do_sample=True,
+    temperature=0.7
+)
+
+# دالة لتقييم الفهم (تشابه الجملة والرد)
+def evaluate_understanding(prompt, response):
+    prompt_emb = embedder.encode(prompt, convert_to_tensor=True)
+    response_emb = embedder.encode(response, convert_to_tensor=True)
+    
+    similarity = util.cos_sim(prompt_emb, response_emb).item()
+    status = "✅ مفهوم جيدًا" if similarity > 0.5 else "❌ لم يُفهم جيدًا"
+    return status + f" (درجة التشابه: {similarity:.2f})"
+
+# دالة التطبيق الأساسية
+def generate_and_evaluate(prompt):
+    result = generator(prompt)[0]["generated_text"]
+    evaluation = evaluate_understanding(prompt, result)
+    return result, evaluation
+
+# واجهة Gradio
+iface = gr.Interface(
+    fn=generate_and_evaluate,
+    inputs=gr.Textbox(label="📝 أدخل تعليمات أو سؤال (Prompt)"),
+    outputs=[
+        gr.Textbox(label="🤖 رد النموذج"),
+        gr.Textbox(label="📊 تقييم الفهم"),
+    ],
+    title="🧠 LLM Prompt Understanding Evaluator",
+    description="أدخل سؤالاً أو تعليمات، وسيتم توليد الرد وتقييم مدى فهم النموذج له تلقائيًا."
+)
+
+iface.launch()