Update app.py

app.py

@@ -1,97 +1,68 @@
-import os
 import gradio as gr
 import requests
-from huggingface_hub import InferenceApi
-import nltk
-from nltk.tokenize import sent_tokenize
-from dotenv import load_dotenv
-
-# Load environment variables from .env file if present
-load_dotenv()
+import base64
+import os
+import time
 
-# Ensure NLTK sentence tokenizer data is downloaded
-nltk.download('punkt')
+# Get your Hugging Face access token from the secrets
+HUGGING_FACE_API_KEY = os.getenv("Bharat2")  # Ensure your access token is properly set
 
-# Retrieve Hugging Face API key from environment variable
-HF_API_KEY = os.getenv('onteddu')
+def generate_images_from_text(text):
+    # Generate an image using Hugging Face's API
+    headers = {
+        "Authorization": f"Bearer {HUGGING_FACE_API_KEY}"
+    }
+   
+    response = requests.post(
+        "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell",  # Correct model name
+        headers=headers,
+        json={"inputs": text}
+    )
+   
+    if response.status_code == 200:
+        # The API response contains the image as raw binary data
+        image_data = response.content
+       
+        # Convert the image to Base64 for embedding in HTML
+        base64_image = base64.b64encode(image_data).decode('utf-8')
+        return f"data:image/png;base64,{base64_image}"
+    else:
+        print(f"Error generating image: {response.text}")
+        return None
 
-if not HF_API_KEY:
-    raise ValueError("Please set the 'HUGGINGFACE_API_KEY' environment variable.")
+def generate_comic(short_story):
+    sentences = short_story.split('. ')
+    images = []
 
-# Initialize Hugging Face Inference API with a suitable image generation model
-# You can choose a different model if preferred
-MODEL_NAME = "black-forest-labs/FLUX.1-schnell"
-inference = InferenceApi(repo_id=MODEL_NAME, token=HF_API_KEY)
+    for sentence in sentences:
+        if sentence.strip():
+            try:
+                # Generate one image for each sentence
+                image_data = generate_images_from_text(sentence)
+                if image_data:
+                    images.append(image_data)
 
-def generate_comic_images(story):
-    """
-    Takes a short story as input, splits it into sentences, generates an image for each sentence,
-    and returns the list of image URLs.
-    """
-    # Split the story into sentences
-    sentences = sent_tokenize(story)
-    
-    if len(sentences) == 0:
-        return ["No sentences found in the input story."]
-    
-    images = []
-    
-    for idx, sentence in enumerate(sentences):
-        # Optionally, you can modify the prompt to better suit comic-style images
-        prompt = f"Comic book style illustration of: {sentence}"
-        
-        try:
-            # Generate image using Hugging Face Inference API
-            output = inference(prompt)
-            
-            # The output can be a URL or binary data depending on the model
-            # Here we assume it's a URL to the generated image
-            if isinstance(output, str):
-                image_url = output
-            elif isinstance(output, dict) and 'image' in output:
-                image_url = output['image']
-            else:
-                image_url = None
-            
-            if image_url:
-                images.append(image_url)
-            else:
-                images.append("Image generation failed for this sentence.")
-        except Exception as e:
-            images.append(f"Error: {str(e)}")
-    
-    return images
+                # Sleep for a short duration to avoid hitting rate limits
+                time.sleep(2)  # Adjust this time if needed
 
-def create_comic_panel(images):
-    """
-    Arranges images in a comic panel layout.
-    """
-    # Create a list of Gradio Image components with captions
-    image_components = []
-    for idx, img in enumerate(images):
-        image_components.append(gr.Image(value=img, label=f"Sentence {idx+1}"))
-    
-    return image_components
+            except Exception as e:
+                print(f"Error generating image: {e}")
 
-def process_story(story):
-    """
-    Main processing function to generate images and arrange them in a comic panel.
-    """
-    images = generate_comic_images(story)
-    image_components = create_comic_panel(images)
-    return image_components
+    # Create comic HTML with reduced image sizes and panel layout
+    comic_html = '<div style="display: flex; flex-wrap: wrap;">'
+    for img in images:
+        comic_html += f'<div style="flex: 1; margin: 5px;"><img src="{img}" style="max-width: 300px; max-height: 300px;" /></div>'
+    comic_html += '</div>'
+   
+    return comic_html
 
-# Define Gradio interface
+# Gradio interface
 iface = gr.Interface(
-    fn=process_story,
-    inputs=gr.Textbox(lines=10, placeholder="Enter your short story here...", label="Short Story"),
-    outputs=gr.Column(),
-    title="Comic Generator",
-    description="Enter a short story, and generate a comic-style image for each sentence using Hugging Face's API.",
-    examples=[
-        ["Once upon a time, there was a brave knight. He fought dragons and rescued princesses."]
-    ]
+    fn=generate_comic,
+    inputs=gr.Textbox(label="Your Short Story", lines=10, placeholder="Enter your short story here..."),
+    outputs=gr.HTML(label="Generated Comic"),
+    title="Narrative to Comic Generator",
+    description="Enter a short story, and the app will generate a comic-style storybook with images and dialogues."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()