Create App.py

App.py

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+import gradio as gr
+import os
+import shutil
+import hashlib
+import subprocess
+import pandas as pd
+from PIL import Image
+from datetime import datetime
+import io
+import base64
+import requests
+import json
+import logging
+
+# Hardcoded Values
+API_KEY = "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"
+IMAGE_FOLDER = "./Images"
+THUMBS_UP_FOLDER = os.path.join(IMAGE_FOLDER, "Thumbs_Up")
+THUMBS_DOWN_FOLDER = os.path.join(IMAGE_FOLDER, "Thumbs_Down")
+BACKUP_FOLDER = "Backup_Scripts"
+ALLOWED_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.gif', '.bmp']
+LOGGING_LEVEL = logging.INFO
+API_URL = "https://api.openai.com/v1/chat/completions"
+
+# Setup logging
+logging.basicConfig(level=LOGGING_LEVEL)
+
+# Ensure necessary directories exist
+os.makedirs(IMAGE_FOLDER, exist_ok=True)
+os.makedirs(THUMBS_UP_FOLDER, exist_ok=True)
+os.makedirs(THUMBS_DOWN_FOLDER, exist_ok=True)
+os.makedirs(BACKUP_FOLDER, exist_ok=True)
+
+def load_gallery(folder):
+    return sorted([os.path.join(folder, f) for f in os.listdir(folder) if os.path.splitext(f)[1].lower() in ALLOWED_EXTENSIONS], key=lambda x: os.path.basename(x).lower())
+
+def get_image_description(image, custom_prompt):
+    if not custom_prompt.strip():
+        custom_prompt = "Describe this image"
+    headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
+    payload = json.dumps({
+        "model": "gpt-4-vision-preview",
+        "messages": [{
+            "role": "user",
+            "content": [{"type": "text", "text": custom_prompt},
+                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encode_image(image)}"}}
+                       ]
+        }],
+        "max_tokens": 300
+    })
+    response = requests.post(API_URL, headers=headers, data=payload)
+    return json.loads(response.text).get('choices', [])[0].get('message', {}).get('content', '')
+
+def encode_image(image):
+    with io.BytesIO() as image_bytes:
+        image.convert('RGB').save(image_bytes, format='JPEG')
+        return base64.b64encode(image_bytes.getvalue()).decode('utf-8')
+
+def save_image_description(image_path, description):
+    text_file_path = f"{os.path.splitext(image_path)[0]}.txt"
+    with open(text_file_path, 'w') as file:
+        file.write(description)
+
+with gr.Blocks() as app:
+    with gr.Row():
+        upload_btn = gr.File(label="Upload Images", type="binary", file_count='multiple')
+        gallery = gr.Gallery(label="Uploaded Images Gallery")
+    upload_btn.change(fn=lambda files: [save_image_description(file.name, get_image_description(Image.open(io.BytesIO(file)), "")) for file in files if file.name.lower().endswith(tuple(ALLOWED_EXTENSIONS))], inputs=upload_btn, outputs=gallery)
+
+    with gr.Accordion("Training Data"):
+        details_df = gr.Dataframe()
+        thumbs_up_gallery = gr.Gallery(value=load_gallery(THUMBS_UP_FOLDER), label="Thumbs Up Gallery")
+        thumbs_down_gallery = gr.Gallery(value=load_gallery(THUMBS_DOWN_FOLDER), label="Thumbs Down Gallery")
+
+        refresh_btn = gr.Button("Refresh")
+        refresh_btn.click(lambda: details_df.update(load_gallery(IMAGE_FOLDER)), inputs=[], outputs=[details_df, thumbs_up_gallery, thumbs_down_gallery])
+
+app.launch()