update ui

.gitignore

@@ -0,0 +1 @@
+.gradio

app.py

@@ -1,64 +1,189 @@
 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)",
-        ),
-    ],
-)
+import torch
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
 
+# Model configuration
+REPO_ID = "forestav/medical_model"
+MODEL_FILE = "unsloth.F16.gguf"
 
+def download_model():
+    """
+    Download the model from Hugging Face Hub if not already present
+    """
+    try:
+        model_path = hf_hub_download(
+            repo_id=REPO_ID,
+            filename=MODEL_FILE,
+            resume_download=True,
+            force_filename=MODEL_FILE
+        )
+        return model_path
+    except Exception as e:
+        print(f"Error downloading model: {e}")
+        return None
+
+def load_model(model_path):
+    """
+    Load the GGUF model using llama_cpp
+    """
+    try:
+        model = Llama(
+            model_path=model_path,
+            n_ctx=4096,  # Adjust context window as needed
+            n_batch=512,  # Batch size for prompt processing
+            verbose=False  # Set to True for detailed loading info
+        )
+        return model
+    except Exception as e:
+        print(f"Error loading model: {e}")
+        return None
+
+def generate_medical_response(model, prompt, max_tokens=300):
+    """
+    Generate a medical advice response using the loaded model
+    """
+    try:
+        # Prepare the prompt with a medical context
+        full_prompt = f"You are a helpful medical AI assistant providing professional medical advice. Respond professionally and carefully:\n\n{prompt}"
+        
+        # Generate response
+        output = model.create_chat_completion(
+            messages=[
+                {"role": "system", "content": "You are a professional medical AI assistant."},
+                {"role": "user", "content": prompt}
+            ],
+            max_tokens=max_tokens,
+            temperature=0.7,
+            top_p=0.9
+        )
+        
+        # Extract and return the response text
+        return output['choices'][0]['message']['content']
+    except Exception as e:
+        return f"An error occurred while generating a response: {e}"
+
+def medical_chatbot_interface(message, history):
+    """
+    Gradio interface function for the medical chatbot
+    """
+    # Ensure model is loaded
+    if not hasattr(medical_chatbot_interface, 'model'):
+        model_path = download_model()
+        if not model_path:
+            return "Failed to download model"
+        
+        medical_chatbot_interface.model = load_model(model_path)
+        if not medical_chatbot_interface.model:
+            return "Failed to load model"
+    
+    # Generate response
+    response = generate_medical_response(medical_chatbot_interface.model, message)
+    return response
+
+# Create Gradio interface with modern, professional medical-themed UI
+def create_medical_chatbot_ui():
+    # Modern, professional medical-themed CSS
+    modern_medical_css = """
+    :root {
+        --primary-color: #2c7da0;
+        --secondary-color: #468faf;
+        --background-color: #f8fbfd;
+        --text-color: #333;
+        --card-background: #ffffff;
+    }
+
+    body {
+        font-family: 'Inter', 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        background-color: var(--background-color);
+        color: var(--text-color);
+        line-height: 1.6;
+    }
+
+    .gradio-container {
+        background-color: var(--background-color);
+        max-width: 800px;
+        margin: 0 auto;
+        padding: 20px;
+        border-radius: 12px;
+        box-shadow: 0 10px 25px rgba(0, 0, 0, 0.05);
+    }
+
+    .chatbot-container {
+        background-color: var(--card-background);
+        border-radius: 12px;
+        border: 1px solid rgba(44, 125, 160, 0.1);
+        overflow: hidden;
+    }
+
+    .message-input {
+        border: 2px solid var(--primary-color);
+        border-radius: 8px;
+        padding: 12px;
+        font-size: 16px;
+        transition: all 0.3s ease;
+    }
+
+    .message-input:focus {
+        outline: none;
+        border-color: var(--secondary-color);
+        box-shadow: 0 0 0 3px rgba(44, 125, 160, 0.1);
+    }
+
+    .submit-button {
+        background-color: var(--primary-color);
+        color: white;
+        border: none;
+        border-radius: 8px;
+        padding: 12px 20px;
+        font-weight: 600;
+        transition: all 0.3s ease;
+    }
+
+    .submit-button:hover {
+        background-color: var(--secondary-color);
+    }
+
+    /* Chat message styling */
+    .message {
+        max-width: 80%;
+        margin: 10px 0;
+        padding: 12px 16px;
+        border-radius: 12px;
+        line-height: 1.5;
+    }
+
+    .user-message {
+        background-color: var(--primary-color);
+        color: white;
+        align-self: flex-end;
+        margin-left: auto;
+    }
+
+    .bot-message {
+        background-color: #f0f4f8;
+        color: var(--text-color);
+        align-self: flex-start;
+    }
+    """
+    
+    # Create Gradio interface with modern design
+    demo = gr.ChatInterface(
+        fn=medical_chatbot_interface,
+        title="🩺 MediAssist: AI Health Companion",
+        description="Get professional medical insights and guidance. Always consult a healthcare professional for personalized medical advice. 🌡️",
+        theme='soft',
+        css=modern_medical_css
+    )
+    
+    return demo
+
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    # Create and launch the Gradio app
+    medical_chatbot = create_medical_chatbot_ui()
+    medical_chatbot.launch(
+        server_name="0.0.0.0",  # Make accessible outside the local machine
+        server_port=7860,
+        share=False  # Generate a public link
+    )