Update app.py

app.py

@@ -5,6 +5,7 @@ import pandas as pd
 import numpy as np
 from datetime import datetime, timedelta
 from typing import Dict, List, Any
+from render_ai_assistant import render_ai_assistant 
 
 # --- Data Processing Class ---
 class DataProcessor:
@@ -377,6 +378,84 @@ def render_chat():
             st.markdown(response)
         st.session_state.messages.append({"role": "assistant", "content": response})
 
+def load_huggingface_model(model_name="google/flan-t5-base"):
+    """
+    Load a pre-trained model from Hugging Face
+    
+    Args:
+        model_name (str): Hugging Face model identifier
+    
+    Returns:
+        tuple: Loaded model and tokenizer
+    """
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        return model, tokenizer
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None, None
+
+def generate_text(model, tokenizer, prompt, max_length=200):
+    """
+    Generate text based on input prompt
+    
+    Args:
+        model: Loaded Hugging Face model
+        tokenizer: Model's tokenizer
+        prompt (str): Input text prompt
+        max_length (int): Maximum generated text length
+    
+    Returns:
+        str: Generated text
+    """
+    inputs = tokenizer(prompt, return_tensors="pt")
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs, 
+            max_length=max_length, 
+            num_return_sequences=1,
+            do_sample=True,
+            temperature=0.7
+        )
+    
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def render_ai_assistant():
+    st.title("🤖 Business AI Assistant")
+    
+    # Model Selection
+    model_options = {
+        "Google Flan-T5": "google/flan-t5-base",
+        "DialoGPT": "microsoft/DialoGPT-medium",
+        "GPT-2 Small": "gpt2"
+    }
+    
+    selected_model = st.selectbox(
+        "Choose AI Model", 
+        list(model_options.keys())
+    )
+    
+    # Load Selected Model
+    model_name = model_options[selected_model]
+    model, tokenizer = load_huggingface_model(model_name)
+    
+    if model and tokenizer:
+        # Prompt Input
+        user_prompt = st.text_area(
+            "Enter your business query", 
+            placeholder="Ask about business strategy, product analysis, etc."
+        )
+        
+        if st.button("Generate Response"):
+            with st.spinner("Generating response..."):
+                response = generate_text(model, tokenizer, user_prompt)
+                st.success("AI Response:")
+                st.write(response)
+    else:
+        st.error("Failed to load model")
+
 def main():
     st.set_page_config(
         page_title="Prospira",
@@ -391,7 +470,7 @@ def main():
         
         page = st.radio(
             "Navigation",
-            ["Dashboard", "Analytics", "Brainstorm", "Chat"]
+            ["Dashboard", "Analytics", "Brainstorm", "AI Assistant", "Chat"]  # Added AI Assistant
         )
     
     if page == "Dashboard":
@@ -400,8 +479,7 @@ def main():
         render_analytics()
     elif page == "Brainstorm":
         render_brainstorm_page()
+    elif page == "AI Assistant":  # New condition
+        render_ai_assistant()
     elif page == "Chat":
-        render_chat()
-
-if __name__ == "__main__":
-    main()
+        render_chat()