Update app.py

app.py

@@ -1,64 +1,72 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from transformers import pipeline
+from datetime import datetime, timezone
 
-"""
-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")
+# Initialize the model
+generator = pipeline('text-generation', model='facebook/opt-350m')  # We'll update this with your medical model later
 
+def get_timestamp():
+    """Get current UTC datetime in specified format"""
+    return datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def format_system_info():
+    """Format system information header"""
+    return (
+        f"Current Date and Time (UTC - YYYY-MM-DD HH:MM:SS formatted): {get_timestamp()}\n"
+        f"Current User's Login: Raj-VedAI\n"
+    )
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def chat(message, history):
+    if history is None:
+        history = []
+    
+    # Add system information
+    system_info = format_system_info()
+    
+    # Format the prompt with system info
+    prompt = f"{system_info}\nPatient Query: {message}\nMedical AI Assistant:"
 
-    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
+    try:
+        # Generate response
+        response = generator(
+            prompt,
+            max_length=512,
+            temperature=0.7,
+            top_p=0.95,
+            do_sample=True
+        )[0]['generated_text']
+        
+        # Extract and format the response
+        ai_response = response[len(prompt):].strip()
+        formatted_response = f"{system_info}\n{ai_response}"
+        
+        history.append((message, formatted_response))
+        return history
+    except Exception as e:
+        return [(message, f"Error: {str(e)}")]
 
+# Create custom theme
+theme = gr.themes.Default().set(
+    body_background_fill="#f0f8ff",  # Light blue background
+    block_background_fill="#ffffff",
+    block_border_width="1px",
+    block_border_color="#2c3e50",
+    block_radius="10px"
+)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Create the Gradio interface
 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)",
-        ),
+    fn=chat,
+    title="Medical Decision Support AI",
+    description="""A medical decision support system that provides healthcare-related information and guidance.
+    Current UTC Time: """ + get_timestamp(),
+    theme=theme,
+    examples=[
+        "What are the symptoms of hypertension?",
+        "What are common drug interactions with aspirin?",
+        "What are the warning signs of diabetes?",
     ],
+    retry_on_error=True
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()