changed gui

app.py

@@ -1,8 +1,8 @@
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModelForSeq2SeqLM
 from PyPDF2 import PdfReader
 
-# Models and tokenizers setup
+# Models and Tokenizers Setup
 models = {
     "Text Generator (Bloom)": {
         "model": AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m"),
@@ -10,98 +10,96 @@ models = {
     },
     "PDF Summarizer (T5)": {
         "model": AutoModelForSeq2SeqLM.from_pretrained("t5-small"),
-        "tokenizer": AutoTokenizer.from_pretrained("t5-small", use_fast=False),  # Use the slow tokenizer
+        "tokenizer": AutoTokenizer.from_pretrained("t5-small", use_fast=False),
     },
     "Broken Answer (T0pp)": {
         "model": AutoModelForSeq2SeqLM.from_pretrained("bigscience/T0pp"),
-        "tokenizer": AutoTokenizer.from_pretrained("bigscience/T0pp", use_fast=False),  # Use the slow tokenizer
+        "tokenizer": AutoTokenizer.from_pretrained("bigscience/T0pp", use_fast=False),
     },
 }
 
+# Chat Function
+def chat_with_model(model_choice, user_message, chat_history, file=None):
+    if model_choice == "PDF Summarizer (T5)" and file is not None:
+        pdf_text = extract_text_from_pdf(file)
+        user_message += f"\n\nPDF Content:\n{pdf_text}"
+
+    if not user_message.strip():
+        return chat_history
 
-# 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()
+    # Tokenize Input
+    inputs = tokenizer(user_message, return_tensors="pt", padding=True, truncation=True, max_length=512)
+    # Generate Output
+    outputs = model.generate(**inputs, max_length=150, num_beams=5, early_stopping=True)
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # Update Chat History
+    chat_history.append((user_message, response))
+    return chat_history
+
+# Function to Extract Text from PDF
+def extract_text_from_pdf(file):
+    from PyPDF2 import PdfReader
+    reader = PdfReader(file.name)
+    text = "\n".join(page.extract_text() for page in reader.pages if page.extract_text())
+    return text
+
+# Interface Setup
+def create_chat_interface():
+    with gr.Blocks(css=""" 
+        .chatbox {
+            background-color: #f7f7f8;
+            border-radius: 12px;
+            padding: 16px;
+            font-family: 'Segoe UI', Tahoma, sans-serif;
+        }
+        .chat-title {
+            font-size: 24px;
+            font-weight: bold;
+            text-align: center;
+            margin-bottom: 12px;
+            color: #3a9fd6;
+        }
+    """) as interface:
+        gr.Markdown("<div class='chat-title'>GPT-Style Chat Interface</div>")
+
+        with gr.Row():
+            model_choice = gr.Dropdown(
+                choices=list(models.keys()),
+                value="Text Generator (Bloom)",
+                label="Select Model"
+            )
+
+        chat_history = gr.Chatbot(label="Chat History", elem_classes="chatbox")
+
+        user_message = gr.Textbox(
+            placeholder="Type your message here...",
+            show_label=False,
+            elem_classes="chatbox",
+        )
+
+        file_input = gr.File(label="Upload PDF", visible=False, file_types=[".pdf"])
+
+        def toggle_pdf_input(selected_model):
+            return gr.update(visible=(selected_model == "PDF Summarizer (T5)"))
+
+        model_choice.change(fn=toggle_pdf_input, inputs=model_choice, outputs=file_input)
+
+        send_button = gr.Button("Send")
+
+        # Link the send button to the chat function
+        send_button.click(
+            chat_with_model,
+            inputs=[model_choice, user_message, chat_history, file_input],
+            outputs=chat_history,
+        )
+
+    return interface
+
+if __name__ == "__main__":
+    interface = create_chat_interface()
+    interface.launch()