Create app.py

app.py

@@ -0,0 +1,117 @@
+import gradio as gr
+from utils import init_model_from, respond
+import tiktoken
+# Configuration
+max_new_tokens = 100
+temperature = 0.5
+top_k = 10
+
+# Model information and links
+
+model_info = {
+    "single_conversation_withGPTdata_bs256": {
+        'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withGPTdata_bs256.pt',
+        'description': "Trained on Facebook Emotion Dialogues dataset with additional GPT data, using a batch size of 256.",
+        'logo': '🧃'
+    },
+    "single_conversation_withGPTdata_withoutemotion": {
+        'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withGPTdata_withoutemotion.pt',
+        'description': "Trained on Facebook Emotion Dialogues dataset with GPT data, excluding emotion tag.",
+        'logo': '🧉'
+    },
+    "single_conversation_withcontext": {
+        'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withcontext.pt',
+        'description': "Trained on Facebook Emotion Dialogues dataset with context included for improved conversational understanding.",
+        'logo': '🍹'
+    },
+    "single_conversation_withemotion": {
+        'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withemotion.pt',
+        'description': "Trained on Facebook Emotion Dialogues dataset, retaining emotion annotations for each conversation.",
+        'logo': '🍺'
+    },
+    "single_conversation_withoutemotion": {
+        'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/singleConversation_withoutemotion.pt',
+        'description': "Trained on Facebook Emotion Dialogues dataset, excluding emotion annotations for simpler conversations.",
+        'logo': '🍷'
+    },
+    "whole_conversation_withoutemotion": {
+        'url': 'https://huggingface.co/HannahLin271/NanoGPT/resolve/main/wholeConversation_withoutemotion.pt',
+        'description': "Trained on entire conversations from the Facebook Emotion Dialogues dataset, excluding tags other than <bot> and <human>.",
+        'logo': '🍵'
+    }
+}
+model_list = { }
+model_choices = list(model_info.keys())
+# init model for default selection
+selected_model_name = "single_conversation_withGPTdata_bs256"
+url = model_info[selected_model_name]['url']
+model_list[selected_model_name] = init_model_from(url, selected_model_name)
+
+# gpt-2 encodings
+print("loading GPT-2 encodings...")
+enc = tiktoken.get_encoding("gpt2")
+encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
+decode = lambda l: enc.decode(l)
+
+
+def predict(input: str,  history: list = None) -> tuple:
+    if history is None:
+        history = []  # Initialize history if not provided
+    # Generate a response using the respond function
+    print(f"selected_model_name: {selected_model_name}")
+    response_data = respond(
+        input=input,
+        samples=1,
+        model=model_list[selected_model_name],
+        encode=encode,
+        decode=decode,
+        max_new_tokens=max_new_tokens,
+        temperature=temperature,
+        top_k=top_k,
+    )
+    
+    response = response_data[1]  # Extract bot's response
+    history.append((input, response))  # Append the user input and bot response to history
+
+    return history, history  # Return updated history twice (for chatbot and state)
+
+def prepare_model(selected_model):
+    global selected_model_name
+    selected_model_name = selected_model
+    url = model_info[selected_model]['url']
+    if selected_model not in model_list:
+        model_list[selected_model] = init_model_from(url, selected_model)
+    logo = model_info[selected_model]['logo']
+    description = model_info[selected_model]['description']
+    return f"## {logo}Model Information\n<br>Model_name: {selected_model}\n<br>Description: {description}"
+
+default_model_info = f"## 🍭Model Information\n<br>Model_name: Name of the model\n<br>Description: How we train the model"
+app = gr.Blocks()
+
+with app:
+    gr.Markdown("# 🫂 Chatbot for ML Project\n### 🤗 Chat with your  chatbot!")
+    # Model Parameters interface
+    inp = gr.Dropdown(
+            choices=model_choices,
+            label="Select a Model",
+            info="Choose a pre-trained model to power the chatbot."
+        )
+    out = gr.Markdown(value=default_model_info)
+    inp.change(prepare_model, inp, out)
+
+    # Chatbot interface
+    chat_interface = gr.Interface(
+        fn=predict,
+        inputs=[
+            gr.Textbox(lines=2, placeholder="Enter your message here...", label="User Input"),
+            gr.State(),  # Maintain conversation state
+        ],
+        outputs=[
+            gr.Chatbot(label="Chatbot Response"),  # Display responses in chat format
+            gr.State()  # Return the updated state
+        ],
+        description="Your AI-based chatbot powered by selected models!"
+    )
+    #TODO: add emotion/context here
+if __name__ == "__main__":
+    app.launch(share=True)