Update app.py

app.py

@@ -1,64 +1,110 @@
-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}]
+# Install required dependencies
+!pip install gradio transformers torch
 
-    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 = ""
+import numpy as np
+import pandas as pd
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
 
-    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
+# Load a free model from Hugging Face
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"  # Small model that works well for simple tasks
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
 
-        response += token
-        yield response
+# Financial knowledge base - simple templates and responses
+financial_templates = {
+    "budget": "Here's a simple budget template based on the 50/30/20 rule:\n- 50% for needs (rent, groceries, utilities)\n- 30% for wants (dining out, entertainment)\n- 20% for savings and debt repayment",
+    "emergency_fund": "An emergency fund should ideally cover 3-6 months of expenses. Start with a goal of $1,000, then build from there.",
+    "debt": "Focus on high-interest debt first (like credit cards). Consider the debt avalanche (highest interest first) or debt snowball (smallest balance first) methods.",
+    "investing": "For beginners, consider index funds or ETFs for diversification. Time in the market beats timing the market.",
+    "retirement": "Take advantage of employer matches in retirement accounts - it's free money. Start early to benefit from compound interest."
+}
 
+# Define guided chat flow
+def guided_response(user_message, chat_history):
+    # Check if we should use a template response
+    for key, template in financial_templates.items():
+        if key in user_message.lower():
+            return template
+    
+    # For more general queries, use the AI model
+    prompt = f"""<human>I need financial advice: {user_message}</human>
+<assistant>"""
+    
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs["input_ids"],
+        max_length=512,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Extract just the assistant's response
+    if "<assistant>" in response:
+        response = response.split("<assistant>")[1].strip()
+    
+    return 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)",
-        ),
-    ],
-)
+# Create budget calculator function
+def calculate_budget(monthly_income, housing, utilities, groceries, transportation):
+    total_needs = housing + utilities + groceries + transportation
+    needs_percent = (total_needs / monthly_income) * 100
+    
+    available_for_wants = monthly_income * 0.3
+    available_for_savings = monthly_income * 0.2
+    
+    return f"""Based on the 50/30/20 rule:
+    
+    Current spending on needs: ${total_needs:.2f} ({needs_percent:.1f}% of income)
+    Recommended max for needs: ${monthly_income * 0.5:.2f} (50%)
+    
+    Available for wants: ${available_for_wants:.2f} (30%)
+    Recommended for savings/debt: ${available_for_savings:.2f} (20%)
+    
+    {'Your needs expenses are within recommended limits!' if needs_percent <= 50 else 'Your needs expenses exceed 50% of income. Consider areas to reduce spending.'}
+    """
 
+# Setup Gradio interface with tabs
+with gr.Blocks() as app:
+    gr.Markdown("# Financial Advisor Bot")
+    
+    with gr.Tab("Chat Advisor"):
+        chatbot = gr.Chatbot(height=400)
+        msg = gr.Textbox(label="Ask a question about personal finance")
+        clear = gr.Button("Clear")
+        
+        def respond(message, chat_history):
+            bot_message = guided_response(message, chat_history)
+            chat_history.append((message, bot_message))
+            return "", chat_history
+        
+        msg.submit(respond, [msg, chatbot], [msg, chatbot])
+        clear.click(lambda: None, None, chatbot, queue=False)
+    
+    with gr.Tab("Budget Calculator"):
+        gr.Markdown("## 50/30/20 Budget Calculator")
+        with gr.Row():
+            income = gr.Number(label="Monthly Income (after tax)")
+        
+        with gr.Row():
+            gr.Markdown("### Monthly Expenses (Needs)")
+        with gr.Row():
+            housing = gr.Number(label="Housing", value=0)
+            utilities = gr.Number(label="Utilities", value=0)
+            groceries = gr.Number(label="Groceries", value=0)
+            transport = gr.Number(label="Transportation", value=0)
+        
+        calculate_btn = gr.Button("Calculate Budget")
+        output = gr.Textbox(label="Budget Analysis", lines=10)
+        
+        calculate_btn.click(
+            calculate_budget, 
+            inputs=[income, housing, utilities, groceries, transport], 
+            outputs=output
+        )
 
-if __name__ == "__main__":
-    demo.launch()
+# Launch the app in Colab
+app.launch(share=True)  # share=True creates a public link you can share with others