Initial commit for themed Gradio app

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from PyPDF2 import PdfReader
+
+# Models and tokenizers setup
+models = {
+    "Text Generator (Bloom)": {
+        "model": AutoModelForSeq2SeqLM.from_pretrained("bigscience/bloom-560m"),
+        "tokenizer": AutoTokenizer.from_pretrained("bigscience/bloom-560m"),
+    },
+    "PDF Summarizer (T5)": {
+        "model": AutoModelForSeq2SeqLM.from_pretrained("t5-small"),
+        "tokenizer": AutoTokenizer.from_pretrained("t5-small"),
+    },
+    "Broken Answer (T0pp)": {
+        "model": AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp"),
+        "tokenizer": AutoTokenizer.from_pretrained("bigscience/T0pp"),
+    },
+}
+
+# Function for text generation
+def generate_text(model_choice, input_text, max_tokens, temperature, top_p):
+    model_info = models[model_choice]
+    tokenizer = model_info["tokenizer"]
+    model = model_info["model"]
+    
+    inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    outputs = model.generate(
+        **inputs, max_length=max_tokens, num_beams=5, early_stopping=True, temperature=temperature, top_p=top_p
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Function for PDF summarization
+def summarize_pdf(pdf_file, max_tokens, temperature, top_p):
+    reader = PdfReader(pdf_file)
+    text = ""
+    for page in reader.pages:
+        text += page.extract_text()
+    
+    model_info = models["PDF Summarizer (T5)"]
+    tokenizer = model_info["tokenizer"]
+    model = model_info["model"]
+    
+    inputs = tokenizer("summarize: " + text, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    outputs = model.generate(
+        **inputs, max_length=max_tokens, num_beams=5, early_stopping=True, temperature=temperature, top_p=top_p
+    )
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Build Gradio interface
+def launch_custom_app():
+    with gr.Blocks(theme=gr.themes.Monochrome()) as demo:
+        gr.Markdown("<h1 style='text-align: center;'>💡 Multi-Model Assistant</h1>")
+        gr.Markdown("<p style='text-align: center;'>Switch between text generation, PDF summarization, or quirky broken answers!</p>")
+        
+        with gr.Tabs():
+            # Tab for Text Generation
+            with gr.Tab("Text Generator"):
+                model_choice = gr.Dropdown(choices=list(models.keys()), label="Choose a Model", value="Text Generator (Bloom)")
+                input_text = gr.Textbox(label="Enter Text")
+                max_tokens = gr.Slider(minimum=10, maximum=512, value=150, step=10, label="Max Tokens")
+                temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
+                top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+                output_text = gr.Textbox(label="Generated Text", interactive=False)
+                generate_button = gr.Button("Generate Text")
+                
+                generate_button.click(
+                    generate_text, 
+                    inputs=[model_choice, input_text, max_tokens, temperature, top_p],
+                    outputs=output_text
+                )
+            
+            # Tab for PDF Summarization
+            with gr.Tab("PDF Summarizer"):
+                pdf_file = gr.File(label="Upload a PDF File", file_types=[".pdf"])
+                max_tokens_pdf = gr.Slider(minimum=10, maximum=512, value=150, step=10, label="Max Tokens")
+                temperature_pdf = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
+                top_p_pdf = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+                summary_output = gr.Textbox(label="PDF Summary", interactive=False)
+                summarize_button = gr.Button("Summarize PDF")
+                
+                summarize_button.click(
+                    summarize_pdf, 
+                    inputs=[pdf_file, max_tokens_pdf, temperature_pdf, top_p_pdf],
+                    outputs=summary_output
+                )
+            
+            # Tab for Broken Model
+            with gr.Tab("Broken Answers"):
+                broken_input = gr.Textbox(label="Enter Text")
+                broken_max_tokens = gr.Slider(minimum=10, maximum=512, value=150, step=10, label="Max Tokens")
+                broken_temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
+                broken_top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)")
+                broken_output = gr.Textbox(label="Broken Model Output", interactive=False)
+                broken_button = gr.Button("Generate Broken Answer")
+                
+                broken_button.click(
+                    lambda text, max_tokens, temp, top_p: generate_text("Broken Answer (T0pp)", text, max_tokens, temp, top_p),
+                    inputs=[broken_input, broken_max_tokens, broken_temperature, broken_top_p],
+                    outputs=broken_output
+                )
+        
+        demo.launch()
+
+# Launch the app
+launch_custom_app()

requirements.txt

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+gradio
+transformers
+PyPDF2