update

.gitignore

@@ -1 +1,2 @@
-venv
+venv
+.gradio

app.py

@@ -1,9 +1,8 @@
 import gradio as gr
 from llama_cpp import Llama
 from huggingface_hub import hf_hub_download
-
-# Global variable to track model loading status
-model = None
+import json
+import re
 
 def load_model():
     repo_id = "forestav/gguf_lora_model"
@@ -24,26 +23,29 @@ def load_model():
     
     return model
 
-def generate_career_response(message, history):
-    if model is None:
-        return "Model is still loading. Please wait..."
-        
-    enhanced_prompt = f"""As a career development advisor, help the user with their professional growth. 
-    Consider:
-    1. Skill development opportunities
-    2. Industry trends
-    3. Practical next steps
-    4. Resources and learning paths
+# Enhanced generation with multiple modes
+def generate_response(message, history, mode='chat'):
+    # Preprocessing based on mode
+    if mode == 'code':
+        system_prompt = "You are an expert coding assistant. Provide clean, efficient code solutions."
+    elif mode == 'creative':
+        system_prompt = "You are a creative writing assistant. Generate imaginative and engaging content."
+    elif mode == 'analytical':
+        system_prompt = "You are an analytical assistant. Provide deep, structured insights and reasoning."
+    else:
+        system_prompt = "You are a helpful AI assistant."
     
-    User Query: {message}
+    # Prepare messages with system context
+    messages = [
+        {"role": "system", "content": system_prompt},
+        *[{"role": "user" if i % 2 == 0 else "assistant", "content": msg} 
+          for i, msg in enumerate(sum(history, []))],
+        {"role": "user", "content": message}
+    ]
     
-    Provide a structured response with actionable advice."""
-
+    # Generate response
     response = model.create_chat_completion(
-        messages=[
-            {"role": "system", "content": "You are a professional career advisor focused on providing practical, actionable guidance for career development."},
-            {"role": "user", "content": enhanced_prompt}
-        ],
+        messages=messages,
         max_tokens=512,
         temperature=0.7,
         top_p=0.95,
@@ -51,53 +53,82 @@ def generate_career_response(message, history):
     
     return response['choices'][0]['message']['content']
 
-# Create the interface first
-demo = gr.ChatInterface(
-    fn=generate_career_response,
-    title="Career Growth Navigator 🚀",
-    description="""Your AI career development partner. Ask about:
-    • Skill development paths
-    • Career transition strategies
-    • Industry trends and opportunities
-    • Resume and interview preparation
-    • Professional networking advice
-    • Work-life balance
-    Let's shape your professional future together!""",
-    examples=[
-        "I'm a software developer wanting to transition into AI/ML. What skills should I focus on?",
-        "How can I improve my leadership skills in my current role?",
-        "What are the key trends in digital marketing I should be aware of?",
-        "I want to start freelancing in web development. Where should I begin?",
-        "How can I negotiate a promotion in my current position?"
-    ]
-)
-
-# Create loading interface
-with gr.Blocks() as loading_demo:
-    gr.Markdown("# Loading Career Growth Navigator 🚀")
-    with gr.Row():
-        loading_msg = gr.Markdown("⏳ The model is currently loading. Please wait...")
+# Extract structured data from text
+def extract_structured_data(text):
+    try:
+        # Try to extract JSON-like structures
+        json_match = re.search(r'\{.*\}', text, re.DOTALL)
+        if json_match:
+            try:
+                return json.loads(json_match.group(0))
+            except json.JSONDecodeError:
+                pass
+        
+        # Fall back to custom parsing for key-value pairs
+        data = {}
+        for line in text.split('\n'):
+            if ':' in line:
+                key, value = line.split(':', 1)
+                data[key.strip()] = value.strip()
+        
+        return data
+    except Exception as e:
+        return {"error": str(e)}
 
-if __name__ == "__main__":
-    # Start with loading interface
-    loading_demo.queue()
-    loading_demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        prevent_thread_lock=True
-    )
-    
-    # Load the model
-    print("Starting model loading...")
-    model = load_model()
-    print("Model loaded successfully!")
+# Create Gradio interface with multiple tabs
+def create_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Multi-Mode AI Assistant")
+        
+        with gr.Tabs():
+            # Chat Interface
+            with gr.TabItem("Conversational Chat"):
+                chat_interface = gr.ChatInterface(
+                    fn=lambda message, history: generate_response(message, history, 'chat'),
+                    title="Conversational AI",
+                    description="General-purpose conversation mode"
+                )
+            
+            # Code Generation Tab
+            with gr.TabItem("Code Assistant"):
+                code_interface = gr.ChatInterface(
+                    fn=lambda message, history: generate_response(message, history, 'code'),
+                    title="AI Code Generator",
+                    description="Generate code snippets and solve programming challenges"
+                )
+            
+            # Creative Writing Tab
+            with gr.TabItem("Creative Writing"):
+                creative_interface = gr.ChatInterface(
+                    fn=lambda message, history: generate_response(message, history, 'creative'),
+                    title="Creative Writing Assistant",
+                    description="Generate stories, poems, and creative content"
+                )
+            
+            # Data Extraction Tab
+            with gr.TabItem("Data Extractor"):
+                with gr.Row():
+                    text_input = gr.Textbox(label="Input Text")
+                    extract_btn = gr.Button("Extract Structured Data")
+                    json_output = gr.JSON(label="Extracted Data")
+                
+                extract_btn.click(
+                    fn=extract_structured_data, 
+                    inputs=text_input, 
+                    outputs=json_output
+                )
     
-    # Close loading interface and launch main interface
-    loading_demo.close()
-    demo.queue()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False
-    )
+    return demo
+
+# Load model globally
+print("Starting model loading...")
+model = load_model()
+print("Model loaded successfully!")
+
+# Create and launch the interface
+demo = create_interface()
+demo.launch(
+    server_name="0.0.0.0",  # Necessary for Spaces
+    server_port=7860,       # Standard port for Spaces
+    share=True            # Don't need share link in Spaces
+)