Update app.py

app.py

@@ -1,64 +1,158 @@
+!pip uninstall accelerate peft bitsandbytes transformers trl -y
+!pip install accelerate peft==0.13.2 bitsandbytes transformers trl==0.12.0
+!pip install huggingface_hub
+!pip install ipywidgets
+!pip install -q gradio
+
+import torch
+from trl import SFTTrainer
+from peft import LoraConfig
+from datasets import load_dataset
+from transformers import (AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, pipeline)
+import ipywidgets as widgets
+from IPython.display import display
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+
+llama_model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path = "aboonaji/llama2finetune-v2",
+                                                   quantization_config = BitsAndBytesConfig(load_in_4bit = True, bnb_4bit_compute_dtype = getattr(torch, "float16"), bnb_4bit_quant_type = "nf4"))
+llama_model.config.use_cache = False
+llama_model.config.pretraining_tp = 1
+
+llama_tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path = "aboonaji/llama2finetune-v2", trust_remote_code = True)
+llama_tokenizer.pad_token = llama_tokenizer.eos_token
+llama_tokenizer.padding_side = "right"
+
+training_arguments = TrainingArguments(output_dir = "./results", per_device_train_batch_size = 1, max_steps = 100)
+llama_sft_trainer = SFTTrainer(model = llama_model,
+                               args = training_arguments,
+                               train_dataset = load_dataset(path = "aboonaji/wiki_medical_terms_llam2_format", split = "train"),
+                               tokenizer = llama_tokenizer,
+                               peft_config = LoraConfig(task_type = "CAUSAL_LM", r = 16, lora_alpha = 16, lora_dropout = 0.1),
+                               dataset_text_field = "text")
+
+llama_sft_trainer.train()
+
+generator = pipeline("text-generation", model=llama_model, tokenizer=llama_tokenizer, max_length=500)
+
+# In-memory user database
+user_db = {}
+
+# Response function
+def generate_response(prompt):
+    response = generator(f"<s>[INST] {prompt} [/INST]")[0]["generated_text"]
+    return response
+
+# Sign-up and login logic
+def signup_user(new_username, new_password):
+    if not new_username or not new_password:
+        return "❌ No input. Please provide both username and password."
+    if new_username in user_db:
+        return "❌ Username already exists."
+    user_db[new_username] = new_password
+    return "✅ Account created! Please log in."
+
+def login_user(username, password):
+    if username in user_db and user_db[username] == password:
+        return (
+            gr.update(visible=True),   # Show chat UI
+            gr.update(visible=False),  # Hide login UI
+            "",                         # Clear login message
+            gr.update(selected=3)      # Switch to Chat tab
+        )
+    return (
+        gr.update(visible=False),
+        gr.update(visible=True),
+        "❌ Invalid credentials",
+        gr.update(selected=2)
+    )
+
+def logout_user():
+    return (
+        gr.update(visible=False),  # Hide chat UI
+        gr.update(visible=True),   # Show login UI
+        gr.update(selected=0)      # Switch to Landing tab
+    )
+
+with gr.Blocks(theme="soft", css="""
+#create-btn button,
+#login-btn button,
+#submit-btn button,
+#logout-btn button {
+    font-size: 12px !important;
+    padding: 4px 8px !important;
+    height: 30px !important;
+    width: auto !important;
+    min-width: 80px !important;
+}
+""") as demo:
+    with gr.Tabs(selected=0, elem_id="tabs") as tabs:
+        with gr.Tab("Home"):
+            with gr.Column(elem_id="landing-container") as landing_ui:
+                gr.Markdown("# 🏠 Welcome to MEDChat AI")
+                gr.Markdown("---")
+                gr.Markdown("#### 🔧 Features:")
+                gr.Markdown("- Medical Q&A support\n- Easy-to-use chatbot interface\n- Privacy-focused with local data")
+                gr.Markdown("---")
+                gr.Markdown("© 2025 MEDChat AI | Finetuned by LLaMA 2 | Created for educational purposes")
+
+        with gr.Tab("Sign Up"):
+            with gr.Column() as signup_ui:
+                gr.Markdown("### 📝 Sign Up")
+                new_username = gr.Textbox(label="New Username")
+                new_password = gr.Textbox(label="New Password", type="password")
+                create_account_btn = gr.Button("Create Account", elem_id="create-btn")
+                signup_msg = gr.Markdown()
+
+        with gr.Tab("Login"):
+            with gr.Column(visible=True) as login_ui:
+                gr.Markdown("### 🔐 Login")
+                username = gr.Textbox(label="Username")
+                password = gr.Textbox(label="Password", type="password")
+                login_btn = gr.Button("Login", elem_id="login-btn")
+                login_msg = gr.Markdown()
+
+        with gr.Tab("Chat"):
+            with gr.Column(visible=False) as chat_ui:
+                gr.Markdown("## 💬 MEDChat AI")
+                gr.Markdown("What can I help with today?")
+                logout_btn = gr.Button("🚪 Logout", elem_id="logout-btn")
+                prompt = gr.Textbox(lines=5, placeholder="Enter your prompt...")
+                submit_btn = gr.Button("Submit", elem_id="submit-btn")
+                response = gr.Textbox(label="Response")
+
+                gr.Markdown("### Try one of these:")
+                examples = gr.Examples(
+                    examples=[
+                        ["What does the immune system do?"],
+                        ["What is Epistaxis?"],
+                        ["Do our intestines contain germs?"],
+                        ["What are allergies?"],
+                        ["Should I start taking creatine?"],
+                        ["What are antibiotics?"],
+                        ["Why do I get sick?"],
+                        ["What's the difference between bacteria and viruses?"],
+                        ["Where are some places that germs hide?"],
+                    ],
+                    inputs=prompt
+                )
+
+
+
+                logout_btn.click(fn=logout_user, outputs=[chat_ui, login_ui, tabs])
+
+    # Tab navigation logic
+    to_signup = gr.Button(visible=False)
+    to_login = gr.Button(visible=False)
+
+    to_signup.click(lambda: gr.update(selected=1), outputs=tabs)
+    to_login.click(lambda: gr.update(selected=2), outputs=tabs)
+    create_account_btn.click(fn=signup_user, inputs=[new_username, new_password], outputs=signup_msg)
+    login_btn.click(
+        fn=login_user,
+        inputs=[username, password],
+        outputs=[chat_ui, login_ui, login_msg, tabs]
+    )
+    submit_btn.click(fn=generate_response, inputs=prompt, outputs=response)
+
+# Launch the interface
+demo.launch(share=True)