Update app.py

app.py

@@ -1,105 +1,79 @@
+import os
 import gradio as gr
 import torch
-from transformers import pipeline
-from gtts import gTTS
-from ultralytics import YOLO
-from openai import OpenAI
 import nltk
-from nltk.tokenize import sent_tokenize
+from openai import OpenAI
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from diffusers import StableDiffusionPipeline
+from ultralytics import YOLO
+from gtts import gTTS
 from PIL import Image
-from io import BytesIO
-import os
+import numpy as np
+from nltk.tokenize import sent_tokenize
+from IPython.display import Audio
 
-# Κατέβασμα απαραίτητων μοντέλων
-nltk.download("punkt")
-client = OpenAI()
+# Βεβαιωθείτε ότι το API Key υπάρχει
+api_key = os.getenv("OPENAI_API_KEY")
+if not api_key:
+    raise ValueError("⚠️ OpenAI API Key is missing! Add it as a Secret in Hugging Face Spaces.")
+
+# OpenAI Client
+client = OpenAI(api_key=api_key)
 
 # Φόρτωση μοντέλων
-yolo_model = YOLO("yolov8n.pt")  # YOLOv8
-summarizer = pipeline("summarization", model="facebook/bart-large-cnn")  # Summarization model
-pipe = pipeline("image-to-image", model="runwayml/stable-diffusion-v1-5")  # Stable Diffusion
+yolo_model = YOLO("yolov8s.pt")
+stable_diffusion = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+nltk.download("punkt")
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
-# 1. Object Detection
-def detect_objects(image):
-    image_path = "uploaded_image.jpg"
-    image.save(image_path)
-    
+def detect_objects(image_path):
     results = yolo_model(image_path)
     detected_objects = []
-    
     for r in results:
         for box in r.boxes:
             class_id = int(box.cls[0])
             label = yolo_model.names[class_id]
             detected_objects.append(label)
+    return detected_objects
 
-    return ", ".join(detected_objects)
-
-# 2. Story Generation
 def generate_story(detected_objects):
-    story_prompt = f"Write a short story based on the following objects: {detected_objects}"
-    
+    story_prompt = f"Write a short story based on the following objects: {', '.join(detected_objects)}"
     response = client.chat.completions.create(
         model="gpt-4o-mini",
         messages=[{"role": "user", "content": story_prompt}],
         max_tokens=200
     )
-
     return response.choices[0].message.content
 
-# 3. Summarization and Scene Splitting
 def summarize_story(story):
     summary = summarizer(story, max_length=100, do_sample=False)[0]['summary_text']
     scenes = sent_tokenize(summary)
     return scenes
 
-# 4. Image Generation
-def generate_images(scenes):
+def generate_images(story):
+    scenes = summarize_story(story)
+    prompts = [f"Highly detailed, cinematic scene: {scene}, digital art, 4K, realistic lighting" for scene in scenes]
     images = []
-    for idx, scene in enumerate(scenes):
-        prompt = f"Highly detailed, cinematic scene: {scene}, digital art, 4K, realistic lighting"
-        image = pipe(prompt).images[0]
-        image_path = f"scene_{idx + 1}.png"
-        image.save(image_path)
-        images.append(image_path)
-
+    for prompt in prompts:
+        image = stable_diffusion(prompt).images[0]
+        images.append(image)
     return images
 
-# 5. Text-to-Speech
 def text_to_speech(story):
     tts = gTTS(text=story, lang="en", slow=False)
-    audio_path = "story_audio.mp3"
-    tts.save(audio_path)
-    return audio_path
-
-# **Τελική Αυτοματοποιημένη Ροή**
-def full_pipeline(image):
-    detected_objects = detect_objects(image)
-    story = generate_story(detected_objects)
-    scenes = summarize_story(story)
-    images = generate_images(scenes)
-    audio = text_to_speech(story)
+    audio_file_path = "story_audio.mp3"
+    tts.save(audio_file_path)
+    return audio_file_path
 
-    return story, scenes, images, audio
+detect_interface = gr.Interface(fn=detect_objects, inputs="image", outputs="text", title="Object Detection")
+story_interface = gr.Interface(fn=generate_story, inputs="text", outputs="text", title="Story Generation")
+summary_interface = gr.Interface(fn=summarize_story, inputs="text", outputs="text", title="Story Summarization")
+image_interface = gr.Interface(fn=generate_images, inputs="text", outputs="image", title="Image Generation")
+audio_interface = gr.Interface(fn=text_to_speech, inputs="text", outputs="audio", title="Text to Speech")
 
-# **Gradio UI**
-demo = gr.Interface(
-    fn=full_pipeline,
-    inputs=gr.Image(type="pil"),
-    outputs=[
-        gr.Textbox(label="Generated Story"),
-        gr.Textbox(label="Story Scenes"),
-        gr.Gallery(label="Generated Images"),
-        gr.Audio(label="Story Audio"),
-    ],
-    title="AI-Powered Storytelling Assistant",
-    description="Upload an image, and the AI will detect objects, generate a story, create images, and narrate the story."
-)
+# Συνδυασμός των interfaces σε ένα Gradio Tabbed Interface
+demo = gr.TabbedInterface([detect_interface, story_interface, summary_interface, image_interface, audio_interface],
+                          ["Detect Objects", "Generate Story", "Summarize Story", "Generate Images", "Text to Speech"])
 
 if __name__ == "__main__":
-    demo.launch()
-
-
-
-
-
+    demo.launch()