Upload app.py

app.py

@@ -0,0 +1,309 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import time
+import bitsandbytes as bnb
+
+print(f"bitsandbytes version: {bnb.__version__}")
+print(f"CUDA is available: {torch.cuda.is_available()}")
+print(f"CUDA device count: {torch.cuda.device_count()}")
+if torch.cuda.is_available():
+    print(f"Current CUDA device: {torch.cuda.current_device()}")
+    print(f"CUDA device name: {torch.cuda.get_device_name(0)}")
+
+class ConversationManager:
+    def __init__(self):
+        self.models = {}
+        self.conversation = []
+        self.delay = 3
+        self.is_paused = False
+        self.current_model = None
+        self.initial_prompt = ""
+        self.task_complete = False
+
+    def load_model(self, model_name):
+        if not model_name:
+            print("Error: Empty model name provided")
+            return None
+    
+        if model_name in self.models:
+            return self.models[model_name]
+
+        try:
+            print(f"Attempting to load model: {model_name}")
+            tokenizer = AutoTokenizer.from_pretrained(model_name)
+            # Try to load the model with 8-bit quantization
+            model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_8bit=True)
+        except RuntimeError as e:
+            print(f"8-bit quantization not available, falling back to full precision: {e}")
+            if torch.cuda.is_available():
+                model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+            else:
+                model = AutoModelForCausalLM.from_pretrained(model_name)
+        except Exception as e:
+            print(f"Failed to load model {model_name}: {e}")
+            print(f"Error type: {type(e).__name__}")
+            print(f"Error details: {str(e)}")
+            return None
+
+        self.models[model_name] = (model, tokenizer)
+        print(f"Successfully loaded model: {model_name}")
+        return self.models[model_name]
+
+    def generate_response(self, model_name, prompt):
+        model, tokenizer = self.load_model(model_name)
+        
+        formatted_prompt = f"Human: {prompt.strip()}\n\nAssistant:"
+        
+        inputs = tokenizer(formatted_prompt, return_tensors="pt", max_length=1024, truncation=True)
+        with torch.no_grad():
+            outputs = model.generate(**inputs, max_length=200, num_return_sequences=1, do_sample=True)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    def add_to_conversation(self, model_name, response):
+        self.conversation.append((model_name, response))
+        if "task complete?" in response.lower():
+            self.task_complete = True
+
+    def get_conversation_history(self):
+        return "\n".join([f"{model}: {msg}" for model, msg in self.conversation])
+
+    def clear_conversation(self):
+        self.conversation = []
+        self.initial_prompt = ""
+        self.models = {}
+        self.current_model = None
+        self.task_complete = False
+
+    def rewind_conversation(self, steps):
+        self.conversation = self.conversation[:-steps]
+        self.task_complete = False
+
+    def rewind_and_insert(self, steps, inserted_response):
+        if steps > 0:
+            self.conversation = self.conversation[:-steps]
+        if inserted_response.strip():
+            last_model = self.conversation[-1][0] if self.conversation else "User"
+            next_model = "Model 1" if last_model == "Model 2" or last_model == "User" else "Model 2"
+            self.conversation.append((next_model, inserted_response))
+        self.current_model = last_model
+        self.task_complete = False
+
+manager = ConversationManager()
+
+def get_model(dropdown, custom):
+    return custom if custom and custom.strip() else dropdown
+
+def chat(model1, model2, user_input, history, inserted_response=""):
+    try:
+        print(f"Starting chat with models: {model1}, {model2}")
+        print(f"User input: {user_input}")
+        
+        model1 = get_model(model1, model1_custom.value)
+        model2 = get_model(model2, model2_custom.value)
+        
+        print(f"Selected models: {model1}, {model2}")
+        
+        if not manager.load_model(model1) or not manager.load_model(model2):
+            return "Error: Failed to load one or both models. Please check the model names and try again.", ""
+        
+        if not manager.conversation:
+            manager.initial_prompt = user_input
+            manager.clear_conversation()
+            manager.add_to_conversation("User", user_input)
+        
+        models = [model1, model2]
+        current_model_index = 0 if manager.current_model in ["User", "Model 2"] else 1
+        
+        while not manager.task_complete:
+            if manager.is_paused:
+                yield history, "Conversation paused."
+                return
+
+            model = models[current_model_index]
+            manager.current_model = model
+            
+            if inserted_response and current_model_index == 0:
+                response = inserted_response
+                inserted_response = ""
+            else:
+                conversation_history = manager.get_conversation_history()
+                prompt = f"{conversation_history}\n\nPlease continue the conversation. If you believe the task is complete, end your response with 'Task complete?'"
+                response = manager.generate_response(model, prompt)
+            
+            manager.add_to_conversation(model, response)
+            history = manager.get_conversation_history()
+            
+            for i in range(manager.delay, 0, -1):
+                yield history, f"{model} is writing... {i}"
+                time.sleep(1)
+            
+            yield history, ""
+            
+            if manager.task_complete:
+                yield history, "Models believe the task is complete. Are you satisfied with the result? (Yes/No)"
+                return
+            
+            current_model_index = (current_model_index + 1) % 2
+        
+        return history, "Conversation completed."
+    except Exception as e:
+        print(f"Error in chat function: {str(e)}")
+        print(f"Error type: {type(e).__name__}")
+        print(f"Error details: {str(e)}")
+        return f"An error occurred: {str(e)}", ""
+
+def user_satisfaction(satisfied, history):
+    if satisfied.lower() == 'yes':
+        return history, "Task completed successfully."
+    else:
+        manager.task_complete = False
+        return history, "Continuing the conversation..."
+
+def pause_conversation():
+    manager.is_paused = True
+    return "Conversation paused. Press Resume to continue."
+
+def resume_conversation():
+    manager.is_paused = False
+    return "Conversation resumed."
+
+def edit_response(edited_text):
+    if manager.conversation:
+        manager.conversation[-1] = (manager.current_model, edited_text)
+    manager.task_complete = False
+    return manager.get_conversation_history()
+
+def restart_conversation(model1, model2, user_input):
+    manager.clear_conversation()
+    return chat(model1, model2, user_input, "")
+
+def rewind_and_insert(steps, inserted_response, history):
+    manager.rewind_and_insert(int(steps), inserted_response)
+    return manager.get_conversation_history(), ""
+
+open_source_models = [
+    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "bigcode/starcoder2-15b",
+    "bigcode/starcoder2-3b",
+    "tiiuae/falcon-7b",
+    "EleutherAI/gpt-neox-20b",
+    "google/flan-ul2",
+    "stabilityai/stablelm-zephyr-3b",
+    "HuggingFaceH4/zephyr-7b-beta",
+    "microsoft/phi-2",
+    "google/gemma-7b-it",
+    "OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
+    "mosaicml/mpt-7b-chat",
+    "databricks/dolly-v2-12b",
+    "thebloke/Wizard-Vicuna-13B-Uncensored-HF",
+    "bigscience/bloom-560m"
+]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# ConversAI Playground")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            model1_dropdown = gr.Dropdown(choices=open_source_models, label="Model 1")
+            model1_custom = gr.Textbox(label="Custom Model 1")
+        with gr.Column(scale=1):
+            model2_dropdown = gr.Dropdown(choices=open_source_models, label="Model 2")
+            model2_custom = gr.Textbox(label="Custom Model 2")
+    
+    user_input = gr.Textbox(label="Initial prompt", lines=2)
+    chat_history = gr.Textbox(label="Conversation", lines=20)
+    current_response = gr.Textbox(label="Current model response", lines=3)
+    
+    with gr.Row():
+        pause_btn = gr.Button("Pause")
+        edit_btn = gr.Button("Edit")
+        rewind_btn = gr.Button("Rewind")
+        resume_btn = gr.Button("Resume")
+        restart_btn = gr.Button("Restart")
+        clear_btn = gr.Button("Clear")
+    
+    with gr.Row():
+        rewind_steps = gr.Slider(0, 10, 1, label="Steps to rewind")
+        inserted_response = gr.Textbox(label="Insert response after rewind", lines=2)
+    
+    delay_slider = gr.Slider(0, 10, 3, label="Response Delay (seconds)")
+
+    user_satisfaction_input = gr.Textbox(label="Are you satisfied with the result? (Yes/No)", visible=False)
+
+    gr.Markdown("""
+    ## Button Descriptions
+    - **Pause**: Temporarily stops the conversation. The current model will finish its response.
+    - **Edit**: Allows you to modify the last response in the conversation.
+    - **Rewind**: Removes the specified number of last responses from the conversation.
+    - **Resume**: Continues the conversation from where it was paused.
+    - **Restart**: Begins a new conversation with the same or different models, keeping the initial prompt.
+    - **Clear**: Resets everything, including loaded models, conversation history, and initial prompt.
+    """)
+
+    def on_chat_update(history, response):
+        if response and "Models believe the task is complete" in response:
+            return gr.update(visible=True), gr.update(visible=False)
+        return gr.update(visible=False), gr.update(visible=True)
+
+    start_btn = gr.Button("Start Conversation")
+    chat_output = start_btn.click(
+        chat,
+        inputs=[
+            model1_dropdown,
+            model2_dropdown,
+            user_input,
+            chat_history
+        ],
+        outputs=[chat_history, current_response]
+    )
+    
+    chat_output.then(
+        on_chat_update,
+        inputs=[chat_history, current_response],
+        outputs=[user_satisfaction_input, start_btn]
+    )
+    
+    user_satisfaction_input.submit(
+        user_satisfaction,
+        inputs=[user_satisfaction_input, chat_history],
+        outputs=[chat_history, current_response]
+    ).then(
+        chat,
+        inputs=[
+            model1_dropdown,
+            model2_dropdown,
+            user_input,
+            chat_history
+        ],
+        outputs=[chat_history, current_response]
+    )
+    
+    pause_btn.click(pause_conversation, outputs=[current_response])
+    resume_btn.click(
+        chat,
+        inputs=[
+            model1_dropdown,
+            model2_dropdown,
+            user_input,
+            chat_history,
+            inserted_response
+        ],
+        outputs=[chat_history, current_response]
+    )
+    edit_btn.click(edit_response, inputs=[current_response], outputs=[chat_history])
+    rewind_btn.click(rewind_and_insert, inputs=[rewind_steps, inserted_response, chat_history], outputs=[chat_history, current_response])
+    restart_btn.click(
+        restart_conversation,
+        inputs=[
+            model1_dropdown,
+            model2_dropdown,
+            user_input
+        ],
+        outputs=[chat_history, current_response]
+    )
+    clear_btn.click(manager.clear_conversation, outputs=[chat_history, current_response, user_input])
+    delay_slider.change(lambda x: setattr(manager, 'delay', x), inputs=[delay_slider])
+
+if __name__ == "__main__":
+    demo.launch()