Update app.py

app.py

@@ -1,99 +1,19 @@
 import gradio as gr
-from huggingface_hub import InferenceClient, HfApi
-import json
-import os
-from datetime import datetime
-from datasets import Dataset
+from huggingface_hub import InferenceClient
 
 # Initialize the InferenceClient with a suitable model
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-hf_api = HfApi()
-
-# Configuration
-LOG_DIR = "conversation_logs"
-TRAINING_DATA_DIR = "training_data"
-MODEL_REPO = "your-username/your-model-repo"  # Change this to your model repo
-
-os.makedirs(LOG_DIR, exist_ok=True)
-os.makedirs(TRAINING_DATA_DIR, exist_ok=True)
-
-# System message template
-system_message = """
-You are a health assistant for Womuna, a platform focused on women's health. 
-Womuna provides educational content on women's health topics, a health journal (blog), 
-product comparisons for health and skincare products, and a community for users to share 
-their experiences and seek support. 
-Your role is to provide accurate and helpful information about health, wellness, and medical 
-topics, and to guide users to relevant resources on Womuna, such as blogs, product comparisons, 
-or the community forum. Always be empathetic and supportive in your responses.
-"""
-
-def log_conversation(user_message, assistant_response):
-    """Log conversations for later training"""
-    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-    log_file = os.path.join(LOG_DIR, f"conversation_{timestamp}.json")
-    
-    log_entry = {
-        "messages": [
-            {"role": "system", "content": system_message},
-            {"role": "user", "content": user_message},
-            {"role": "assistant", "content": assistant_response}
-        ]
-    }
-    
-    with open(log_file, 'w') as f:
-        json.dump(log_entry, f, indent=2)
-
-def prepare_training_data():
-    """Prepare training data from logged conversations"""
-    training_examples = []
-    
-    for log_file in os.listdir(LOG_DIR):
-        if log_file.endswith('.json'):
-            try:
-                with open(os.path.join(LOG_DIR, log_file), 'r') as f:
-                    data = json.load(f)
-                    training_examples.append(data)
-            except Exception as e:
-                print(f"Error processing {log_file}: {e}")
-    
-    if training_examples:
-        # Create a dataset
-        dataset = Dataset.from_list(training_examples)
-        
-        # Save as JSONL file
-        timestamp = datetime.now().strftime("%Y%m%d")
-        output_file = os.path.join(TRAINING_DATA_DIR, f"training_data_{timestamp}.jsonl")
-        dataset.to_json(output_file)
-        
-        return output_file
-    return None
-
-def fine_tune_model():
-    """Trigger fine-tuning with new data"""
-    training_file = prepare_training_data()
-    if not training_file:
-        print("No new training data available")
-        return False
-    
-    try:
-        # In a real implementation, you would:
-        # 1. Upload the training file to Hub
-        # 2. Trigger a fine-tuning job
-        # This is a placeholder for the actual implementation
-        
-        print(f"Starting fine-tuning with {training_file}")
-        # hf_api.create_repo(repo_id=MODEL_REPO, exist_ok=True)
-        # hf_api.upload_file(path_or_fileobj=training_file, path_in_repo="training_data.jsonl", repo_id=MODEL_REPO)
-        # Then trigger training job...
-        
-        # For now, we'll just print that we would train
-        return True
-    except Exception as e:
-        print(f"Error during fine-tuning: {e}")
-        return False
 
 def respond(message, history: list[tuple[str, str]]):
+    system_message = """
+    You are a health assistant for Womuna, a platform focused on women's health. 
+    Womuna provides educational content on women's health topics, a health journal (blog), 
+    product comparisons for health and skincare products, and a community for users to share 
+    their experiences and seek support. 
+    Your role is to provide accurate and helpful information about health, wellness, and medical 
+    topics, and to guide users to relevant resources on Womuna, such as blogs, product comparisons, 
+    or the community forum. Always be empathetic and supportive in your responses.
+    """
     messages = [{"role": "system", "content": system_message}]
 
     for val in history:
@@ -108,19 +28,16 @@ def respond(message, history: list[tuple[str, str]]):
     if "product comparison" in message.lower():
         response = "You can find detailed product comparisons for health and skincare products on Womuna's product comparison section. Visit [Womuna Product Comparisons](https://womuna.com/shop/) to make informed buying decisions."
         yield response
-        log_conversation(message, response)
         return
 
     if "community" in message.lower() or "support" in message.lower():
         response = "Womuna has a built-in community where you can share your experiences, seek advice, and get moral support from other users. Visit [Womuna Community](https://womuna.com/womunity/) to join the conversation."
         yield response
-        log_conversation(message, response)
         return
 
     if "blog" in message.lower() or "journal" in message.lower():
         response = "Womuna's health journal offers a wealth of educational content on women's health topics. Check out the latest posts at [Womuna Blog](https://womuna.com/)."
         yield response
-        log_conversation(message, response)
         return
 
     # Default response for general health queries
@@ -135,15 +52,8 @@ def respond(message, history: list[tuple[str, str]]):
         token = message.choices[0].delta.content
         response += token
         yield response
-    
-    # Log the conversation after it's complete
-    log_conversation(message, response)
-
-    # Periodically trigger fine-tuning (e.g., every 100 conversations)
-    if len(os.listdir(LOG_DIR)) % 100 == 0:
-        fine_tune_model()
 
-# Custom CSS (same as before)
+# Custom CSS for a modern UI
 css = """
 .gradio-container {
     font-family: 'Arial', sans-serif;
@@ -211,29 +121,20 @@ footer {
 }
 """
 
-# Add a button for manual training trigger
-with gr.Blocks(css=css) as demo:
-    gr.Markdown("# MedAI Health Assistant")
-    gr.Markdown("Ask me anything about women's health, wellness, and medical topics.")
-    
-    chatbot = gr.ChatInterface(
-        respond,
-        examples=[
-            "What are the best skincare products for sensitive skin?",
-            "Can you recommend a good blog post about menstrual health?",
-            "Where can I find support for postpartum depression?",
-            "What is PCOS?"
-        ]
-    )
-    
-    with gr.Row():
-        train_btn = gr.Button("Train with New Conversations")
-        train_output = gr.Textbox(label="Training Status")
-    
-    train_btn.click(
-        fn=lambda: "Training started successfully!" if fine_tune_model() else "No new data to train on",
-        outputs=train_output
-    )
+# Customize the ChatInterface
+demo = gr.ChatInterface(
+    respond,
+    title="MedAI Health Assistant",
+    description="Ask me anything about women's health, wellness, and medical topics.",
+    css=css,
+    examples=[
+        "What are the best skincare products for sensitive skin?",
+        "Can you recommend a good blog post about menstrual health?",
+        "Where can I find support for postpartum depression?",
+        "What is PCOS?"
+    ],
+    theme="default"
+)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()