update app.py

app.py

@@ -1,53 +1,88 @@
 import torch
 import gradio as gr
-from diffusers import StableDiffusionPipeline
+from transformers import pipeline, AutoModelForSeq2SeqLM, AutoTokenizer, AutoConfig
 
-# List of models for text-to-image generation
+# List of summarization models
 model_names = [
-    "runwayml/stable-diffusion-v1-5",
-    "stabilityai/stable-diffusion-2-1",
-    "CompVis/stable-diffusion-v1-4"
+    "google/bigbird-pegasus-large-arxiv",
+    "facebook/bart-large-cnn",
+    "google/t5-v1_1-large",
+    "sshleifer/distilbart-cnn-12-6",
+    "allenai/led-base-16384",
+    "google/pegasus-xsum",
+    "togethercomputer/LLaMA-2-7B-32K"
 ]
 
-# Placeholder for pipeline
-pipe = None
+# Placeholder for the summarizer pipeline, tokenizer, and maximum tokens
+summarizer = None
+tokenizer = None
+max_tokens = None
 
-# Function to load model
+# Example text for summarization
+example_text = (
+    "Artificial intelligence (AI) is intelligence—perceiving, synthesizing, and inferring information—"
+    "demonstrated by machines, as opposed to intelligence displayed by non-human animals and humans. "
+    "Example tasks in which AI is employed include speech recognition, computer vision, language translation, "
+    "autonomous vehicles, and game playing. AI research has been defined as the field of study of intelligent "
+    "agents, which refers to any system that perceives its environment and takes actions that maximize its "
+    "chance of achieving its goals."
+)
+
+# Function to load the selected model
 def load_model(model_name):
-    global pipe
+    global summarizer, tokenizer, max_tokens
     try:
-        pipe = StableDiffusionPipeline.from_pretrained(model_name, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
-        pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu")
-        return f"Model {model_name} loaded successfully!"
+        # Load the summarization pipeline with the selected model
+        summarizer = pipeline("summarization", model=model_name, torch_dtype=torch.float32)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        config = AutoConfig.from_pretrained(model_name)
+
+        # Set a reasonable default for max_tokens if not available
+        max_tokens = getattr(config, 'max_position_embeddings', 1024)
+
+        return f"Model {model_name} loaded successfully! Max tokens: {max_tokens}"
     except Exception as e:
-        return f"Failed to load model: {str(e)}"
+        return f"Failed to load model {model_name}. Error: {str(e)}"
+
+# Function to summarize the input text
+def summarize_text(input, min_length, max_length):
+    if summarizer is None:
+        return "No model loaded!"
 
-# Function to generate image from text
-def generate_image(prompt, guidance_scale):
-    if pipe is None:
-        return None, "Model not loaded yet!"
     try:
-        image = pipe(prompt, guidance_scale=guidance_scale).images[0]
-        return image, "Image generated successfully!"
+        # Tokenize the input text and check the number of tokens
+        input_tokens = tokenizer.encode(input, return_tensors="pt")
+        num_tokens = input_tokens.shape[1]
+        if num_tokens > max_tokens:
+            return f"Error: Input exceeds the max token limit of {max_tokens}."
+
+        # Ensure min/max lengths are within bounds
+        min_summary_length = max(10, int(num_tokens * (min_length / 100)))
+        max_summary_length = min(max_tokens, int(num_tokens * (max_length / 100)))
+
+        # Summarize the input text
+        output = summarizer(input, min_length=min_summary_length, max_length=max_summary_length, truncation=True)
+        return output[0]['summary_text']
     except Exception as e:
-        return None, f"Image generation failed: {str(e)}"
+        return f"Summarization failed: {str(e)}"
 
-# Gradio UI
+# Gradio Interface
 with gr.Blocks() as demo:
     with gr.Row():
-        model_dropdown = gr.Dropdown(choices=model_names, label="Choose a model", value="runwayml/stable-diffusion-v1-5")
+        model_dropdown = gr.Dropdown(choices=model_names, label="Choose a model", value="sshleifer/distilbart-cnn-12-6")
         load_button = gr.Button("Load Model")
-    load_status = gr.Textbox(label="Status", interactive=False)
 
-    with gr.Row():
-        prompt_input = gr.Textbox(label="Enter text prompt", placeholder="A futuristic city in the sky", lines=2)
-        guidance_slider = gr.Slider(minimum=1, maximum=20, value=7.5, step=0.5, label="Guidance Scale")
-    
-    generate_button = gr.Button("Generate Image")
-    output_image = gr.Image(label="Generated Image")
-    message = gr.Textbox(label="Generation Status", interactive=False)
-
-    load_button.click(fn=load_model, inputs=model_dropdown, outputs=load_status)
-    generate_button.click(fn=generate_image, inputs=[prompt_input, guidance_slider], outputs=[output_image, message])
+    load_message = gr.Textbox(label="Load Status", interactive=False)
+
+    min_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Minimum Summary Length (%)", value=10)
+    max_length_slider = gr.Slider(minimum=0, maximum=100, step=1, label="Maximum Summary Length (%)", value=20)
+
+    input_text = gr.Textbox(label="Input text to summarize", lines=6, value=example_text)
+    summarize_button = gr.Button("Summarize Text")
+    output_text = gr.Textbox(label="Summarized text", lines=4)
+
+    load_button.click(fn=load_model, inputs=model_dropdown, outputs=load_message)
+    summarize_button.click(fn=summarize_text, inputs=[input_text, min_length_slider, max_length_slider],
+                           outputs=output_text)
 
 demo.launch()