Update app.py

app.py

@@ -1,93 +1,97 @@
-import requests
-from PIL import Image
-import gradio as gr
-from transformers import BlipProcessor, BlipForConditionalGeneration, AutoModelForCausalLM, AutoTokenizer, GPT2LMHeadModel, TextGenerationPipeline
-import torch
-import tempfile
-
-# Initialize the device
-device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-# Load the image captioning model and processor
-image_processor = BlipProcessor.from_pretrained("AdhamEhab/ImageCaptioning")
-image_model = BlipForConditionalGeneration.from_pretrained("AdhamEhab/ImageCaptioning").to(device)
-
-# Load the story generation model and tokenizer
-story_generation_model = GPT2LMHeadModel.from_pretrained("AdhamEhab/StoryGen")
-story_generation_tokenizer = AutoTokenizer.from_pretrained("AdhamEhab/StoryGen")
-
-generator = TextGenerationPipeline(model=story_generation_model, tokenizer=story_generation_tokenizer)
-
-# Define a function to generate caption from an image
-def generate_caption(image):
-    try:
-        # If image is a file object, extract the file path
-        if isinstance(image, tempfile._TemporaryFileWrapper):
-            image_path = image.name
-        else:
-            image_path = image
-
-        # Load and preprocess the image
-        image = Image.open(image_path)
-        inputs = image_processor(image, return_tensors="pt", padding="max_length", truncation=True)
-
-        # Generate caption
-        with torch.no_grad():
-            caption_ids = image_model.generate(**inputs.to(device))
-
-        # Decode the caption
-        caption = image_processor.decode(caption_ids[0], skip_special_tokens=True)
-        return caption
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-# Define a function to generate a story based on a prompt
-def generate_story(prompt):
-    try:
-        input_prompt = prompt
-        story = generator(input_prompt, max_length=200, do_sample=True)[0]['generated_text']
-        return story
-    except Exception as e:
-        return f"An error occurred: {str(e)}"
-
-# Create Gradio interfaces
-image_caption_interface = gr.Interface(
-    fn=generate_caption,
-    inputs=gr.inputs.Image(type="filepath", label="Upload Image"),
-    outputs="text",
-    title="Image Captioning",
-    description="Generate a caption for the provided image."
-)
-
-story_generation_interface = gr.Interface(
-    fn=generate_story,
-    inputs="text",
-    outputs="text",
-    title="Story Generation",
-    description="Generate a story based on the provided prompt."
-)
-
-# Create Gradio interfaces
-with gr.Blocks(css="footer{display:none !important}") as combined_interface:
-    gr.Markdown(
-    """
-    # The Seer's Legacy
-    Crafting tales from the fabric of imagination.
-    """)
-    with gr.Row():
-        with gr.Column():
-            image_input = gr.inputs.Image(type="filepath", label="Upload Image")
-            image_output = gr.Text(label="Image Caption")
-            image_btn = gr.Button("Generate Image Caption")
-
-        with gr.Column():
-            text_input = gr.inputs.Textbox(label="Story Prompt")
-            story_output = gr.Text(label="Generated Story")
-            story_btn = gr.Button("Generate Story")
-
-    image_btn.click(generate_caption, inputs=[image_input], outputs=[image_output])
-    story_btn.click(generate_story, inputs=[text_input], outputs=[story_output])
-
-# Launch the combined interface
-combined_interface.launch()
-
+import requests
+from PIL import Image
+import gradio as gr
+from transformers import BlipProcessor, BlipForConditionalGeneration, AutoModelForCausalLM, AutoTokenizer, GPT2LMHeadModel, TextGenerationPipeline
+import torch
+import tempfile
+import os
+
+os.system("pip uninstall -y gradio")
+os.system("pip install gradio==2.6.4")
+
+# Initialize the device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Load the image captioning model and processor
+image_processor = BlipProcessor.from_pretrained("AdhamEhab/ImageCaptioning")
+image_model = BlipForConditionalGeneration.from_pretrained("AdhamEhab/ImageCaptioning").to(device)
+
+# Load the story generation model and tokenizer
+story_generation_model = GPT2LMHeadModel.from_pretrained("AdhamEhab/StoryGen")
+story_generation_tokenizer = AutoTokenizer.from_pretrained("AdhamEhab/StoryGen")
+
+generator = TextGenerationPipeline(model=story_generation_model, tokenizer=story_generation_tokenizer)
+
+# Define a function to generate caption from an image
+def generate_caption(image):
+    try:
+        # If image is a file object, extract the file path
+        if isinstance(image, tempfile._TemporaryFileWrapper):
+            image_path = image.name
+        else:
+            image_path = image
+
+        # Load and preprocess the image
+        image = Image.open(image_path)
+        inputs = image_processor(image, return_tensors="pt", padding="max_length", truncation=True)
+
+        # Generate caption
+        with torch.no_grad():
+            caption_ids = image_model.generate(**inputs.to(device))
+
+        # Decode the caption
+        caption = image_processor.decode(caption_ids[0], skip_special_tokens=True)
+        return caption
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Define a function to generate a story based on a prompt
+def generate_story(prompt):
+    try:
+        input_prompt = prompt
+        story = generator(input_prompt, max_length=200, do_sample=True)[0]['generated_text']
+        return story
+    except Exception as e:
+        return f"An error occurred: {str(e)}"
+
+# Create Gradio interfaces
+image_caption_interface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.inputs.Image(type="filepath", label="Upload Image"),
+    outputs="text",
+    title="Image Captioning",
+    description="Generate a caption for the provided image."
+)
+
+story_generation_interface = gr.Interface(
+    fn=generate_story,
+    inputs="text",
+    outputs="text",
+    title="Story Generation",
+    description="Generate a story based on the provided prompt."
+)
+
+# Create Gradio interfaces
+with gr.Blocks(css="footer{display:none !important}") as combined_interface:
+    gr.Markdown(
+    """
+    # The Seer's Legacy
+    Crafting tales from the fabric of imagination.
+    """)
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.inputs.Image(type="filepath", label="Upload Image")
+            image_output = gr.Text(label="Image Caption")
+            image_btn = gr.Button("Generate Image Caption")
+
+        with gr.Column():
+            text_input = gr.inputs.Textbox(label="Story Prompt")
+            story_output = gr.Text(label="Generated Story")
+            story_btn = gr.Button("Generate Story")
+
+    image_btn.click(generate_caption, inputs=[image_input], outputs=[image_output])
+    story_btn.click(generate_story, inputs=[text_input], outputs=[story_output])
+
+# Launch the combined interface
+combined_interface.launch()
+