Update app.py

app.py

@@ -1,117 +1,83 @@
-# Import the necessary libraries
 import gradio as gr
-import openai
+import requests
+import os
 import base64
+from PIL import Image
 import io
-import os 
-import requests
-
 
-# Function to encode the image to base64
+# Функция для кодирования изображения в base64
 def encode_image_to_base64(image):
     buffered = io.BytesIO()
     image.save(buffered, format="JPEG")
     img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
     return img_str
 
-
-# Function to send the image to the OpenAI API and get a response
-def ask_openai_with_image(instruction, json_prompt, low_quality_mode, image):
-    # Set the OpenAI API key
-    openai.api_key = os.getenv("API_KEY")
-
-    # Encode the uploaded image to base64
+# Функция для отправки запроса в OpenAI с изображением и получения ответа
+def ask_openai_with_image(instruction, image):
+    # Кодируем загруженное изображение в base64
     base64_image = encode_image_to_base64(image)
 
+    # Убираем пробелы с начала и конца инструкции
     instruction = instruction.strip()
 
-    if json_prompt.strip() != "":
-        instruction = f"{instruction}\n\nReturn in JSON format and include the following attributes:\n\n{json_prompt.strip()}"
-
-    # Create the payload with the base64 encoded image
+    # Создаем данные для запроса с закодированным изображением
     payload = {
         "model": "gpt-4-vision-preview",
         "messages": [
             {
                 "role": "user",
-                "content": [
-                    {
-                        "type": "text",
-                        "text": instruction,
-                    },
-                    {
-                        "type": "image_url",
-                        "image_url": {
-                            "url": f"data:image/jpeg;base64,{base64_image}",
-                            "detail": "low" if low_quality_mode else "high",
-                        },
-                    },
-                ],
+                "content": instruction,
+            },
+            {
+                "role": "system",
+                "content": f"data:image/jpeg;base64,{base64_image}",
             }
         ],
-        "max_tokens": 4095,
+        "max_tokens": 5095,
     }
 
-    # Send the request to the OpenAI API
-    response = requests.post(
-        "https://api.openai.com/v1/chat/completions",
-        headers={"Authorization": f"Bearer {openai.api_key}"},
-        json=payload,
-    )
+    # API ключ для OpenAI
+    api_key = os.getenv("API_KEY")
+
+    # Заголовки для запроса
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json',
+    }
+
+    # URL для запроса к API OpenAI
+    url = "https://api.openai.com/v1/chat/completions"
+
+    # Отправляем запрос в OpenAI
+    response = requests.post(url, headers=headers, json=payload)
 
-    # Check if the request was successful
+    # Проверяем ответ и возвращаем результат
     if response.status_code == 200:
         response_json = response.json()
-        print("Response JSON:", response_json)  # Print the raw response JSON
         try:
-            # Attempt to extract the content text
+            # Пытаемся извлечь текст из ответа
             return response_json["choices"][0]["message"]["content"]
         except Exception as e:
-            # If there is an error in the JSON structure, print it
-            print("Error in JSON structure:", e)
-            print("Full JSON response:", response_json)
-            return "Error processing the image response."
+            # Если есть ошибка в структуре JSON, выводим ее
+            return f"Error processing the image response: {e}"
     else:
-        # If an error occurred, return the error message
-        return f"Error: {response.text}"
-
-
-json_schema = gr.Textbox(
-    label="JSON Attributes",
-    info="Define a list of attributes to force the model to respond in valid json format. Leave blank to disable json formatting.",
-    lines=3,
-    placeholder="""Example: 
-- name: Name of the object
-- color: Color of the object
-""",
-)
-
-instructions = gr.Textbox(
-    label="Instructions",
-    info="Instructions for the vision model to follow. Leave blank to use default.",
-    lines=2,
-    placeholder="""Default:
-I've uploaded an image and I'd like to know what it depicts and any interesting details you can provide.""",
-)
-
-low_quality_mode = gr.Checkbox(
-    label="Low Quality Mode",
-    info="See here: https://platform.openai.com/docs/guides/vision/low-or-high-fidelity-image-understanding.",
-)
-
-# Create a Gradio interface
-vision_playground = gr.Interface(
-    fn=ask_openai_with_image,
-    inputs=[
-        instructions,
-        json_schema,
-        low_quality_mode,
-        gr.Image(type="pil", label="Image"),
-    ],
-    outputs=[gr.Markdown()],
-    title="GPT-4-Vision Playground",
-    description="Upload an image and get a description from GPT-4 with Vision.",
-)
+        # Если произошла ошибка, возвращаем сообщение об ошибке
+        return f"Error: {response.status_code} - {response.text}"
+
+# Создаем интерфейс с помощью Gradio
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            instructions = gr.Textbox(label="Instructions", placeholder="Enter the instructions here...")
+            image_input = gr.Image(label="Upload an image", type="pil")
+            submit_button = gr.Button("Submit")
+        with gr.Column():
+            output_markdown = gr.Markdown(label="AI Response")
+
+    submit_button.click(
+        fn=ask_openai_with_image,
+        inputs=[instructions, image_input],
+        outputs=[output_markdown]
+    )
 
-# Launch the app
-vision_playground.launch()
+demo.launch()