Update app.py

app.py

@@ -1,4 +1,3 @@
-import os
 import gradio as gr
 import numpy as np
 import matplotlib.pyplot as plt
@@ -6,53 +5,70 @@ from skimage.transform import radon, iradon
 from scipy.fft import fft, ifft
 from io import BytesIO
 import base64
-from PIL import Image
+import os
 import requests
+from PIL import Image
 
-# Hugging Face API settings
-HF_TOKEN = os.environ.get("HF_TOKEN")  # Fetching the token from environment variables
+# Hugging Face API ayarları
 API_URL = "https://api-inference.huggingface.co/models/openai-community/gpt2-large"
-headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+HF_TOKEN = os.environ.get("HF_TOKEN")  # Environment variable'dan token'ı al
+headers = {"Authorization": f"Bearer {HF_TOKEN}"}  # API anahtarını başlıkta gönder
 
 def query_gpt2(payload):
-    response = requests.post(API_URL, headers=headers, json=payload)
-    return response.json()
+    """Hugging Face API'sine istek gönderir ve yanıtı döner."""
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload)
+        print("Status Code:", response.status_code)  # Durum kodunu yazdır
+        response.raise_for_status()  # Hata durumunda istisna fırlatır
+        response_json = response.json()
+        print("Response JSON:", response_json)  # Yanıtın içeriğini yazdır
+        return response_json
+    except requests.exceptions.HTTPError as errh:
+        print(f"HTTP Error: {errh}")
+    except requests.exceptions.ConnectionError as errc:
+        print(f"Error Connecting: {errc}")
+    except requests.exceptions.Timeout as errt:
+        print(f"Timeout Error: {errt}")
+    except requests.exceptions.RequestException as err:
+        print(f"Something went wrong: {err}")
+    return {"generated_text": "No response from GPT-2."}
 
 def process_and_query(image):
-    # Processing the original image (convert to grayscale)
-    image = image.convert("L")  # Convert to grayscale
+    """Görüntüyü işleyip GPT-2'ye gönderir."""
+    # Orijinal görüntüyü işleme (grayscale'e çevirme)
+    image = image.convert("L")  # Grayscale'e çevirme
     image = np.array(image)
 
-    # Create a sinogram (projection data)
+    # Sinogram oluşturma (projeksiyon verileri)
     theta = np.linspace(0., 180., max(image.shape), endpoint=False)
     sinogram = radon(image, theta=theta, circle=True)
 
-    # Extract sinogram data as text
+    # Sinogram verilerini metin olarak çıkarma
     sinogram_text = "\n".join([", ".join(map(str, row)) for row in sinogram])
-    print("Sinogram Data (Text):")
+    print("Sinogram Verileri (Text):")
     print(sinogram_text)
 
-    # Send the sinogram data to GPT-2 model
+    # GPT-2 modeline sinogram verilerini gönderme
     gpt_response = query_gpt2({"inputs": sinogram_text})
-    gpt_output = gpt_response.get("generated_text", "No response from GPT-2.")
+    gpt_output = gpt_response.get("generated_text", "GPT-2'den yanıt alınamadı.")
 
-    # Apply Fourier transform to the projection data
+    # Projeksiyon verilerine Fourier dönüşümü uygulama
     fourier = fft(sinogram, axis=0)
 
-    # Apply ramp filter
+    # Ramp filtre uygulama
     freq = np.fft.fftfreq(sinogram.shape[0]).reshape(-1, 1)
     ramp_filter = np.abs(freq)
     filtered_fourier = fourier * ramp_filter
 
-    # Apply inverse Fourier transform
+    # Ters Fourier dönüşümü uygulama
     filtered_sinogram = np.real(ifft(filtered_fourier, axis=0))
 
-    # Reconstruct the image using back projection
+    # Geri yansıtma (back projection) ile görüntü oluşturma
     reconstructed_image = iradon(filtered_sinogram, theta=theta, circle=True)
 
-    # Visualization for images
+    # Görselleştirme için görüntüler
     fig, axes = plt.subplots(2, 2, figsize=(10, 10))
-    axes[0, 0].set_title("Original Image")
+    axes[0, 0].set_title("Orijinal Görüntü")
     axes[0, 0].imshow(image, cmap="gray")
     axes[0, 0].axis("off")
 
@@ -60,17 +76,17 @@ def process_and_query(image):
     axes[0, 1].imshow(sinogram, cmap="gray", aspect="auto")
     axes[0, 1].axis("off")
 
-    axes[1, 0].set_title("Filtered Sinogram")
+    axes[1, 0].set_title("Filtrelenmiş Sinogram")
     axes[1, 0].imshow(filtered_sinogram, cmap="gray", aspect="auto")
     axes[1, 0].axis("off")
 
-    axes[1, 1].set_title("Reconstructed Image")
+    axes[1, 1].set_title("Rekonstürülen Görüntü")
     axes[1, 1].imshow(reconstructed_image, cmap="gray")
     axes[1, 1].axis("off")
 
     plt.tight_layout()
 
-    # Return the image and sinogram data as base64 encoded
+    # Görüntüleri base64 formatında döndürme
     buf = BytesIO()
     plt.savefig(buf, format="png")
     buf.seek(0)
@@ -78,20 +94,21 @@ def process_and_query(image):
     buf.close()
     plt.close()
 
+    # Görüntü ve sinogram verisini döndürme
     return f"<img src='data:image/png;base64,{encoded_image}'/>", sinogram_text, gpt_output
 
-# Gradio interface setup
+# Gradio arayüzü tanımlama
 with gr.Blocks() as demo:
-    gr.Markdown("# Sinogram Visualization and GPT-2 Processing")
-    gr.Markdown("Upload an image, process the sinogram data, and send it to GPT-2.")
+    gr.Markdown("# Sinogram Görüntüleme ve GPT-2 İşlemleri")
+    gr.Markdown("Bir görüntü yükleyin, sinogram verilerini işleyin ve GPT-2'ye gönderin.")
 
     with gr.Row():
-        image_input = gr.Image(type="pil", label="Upload Image")
-        output = gr.HTML(label="Result Visualization")
-        sinogram_output = gr.Textbox(label="Sinogram Data (Text)")
-        gpt_output = gr.Textbox(label="GPT-2 Response")
+        image_input = gr.Image(type="pil", label="Görüntü Yükle")
+        output = gr.HTML(label="Sonuç Görselleştirme")
+        sinogram_output = gr.Textbox(label="Sinogram Verileri (Text)")
+        gpt_output = gr.Textbox(label="GPT-2 Yanıtı")
 
-    process_button = gr.Button("Process and Send to GPT-2")
+    process_button = gr.Button("İşle ve GPT-2'ye Gönder")
 
     process_button.click(process_and_query, inputs=[image_input], outputs=[output, sinogram_output, gpt_output])