Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,56 +1,112 @@
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-import torch
 from PIL import Image, ImageDraw, ImageFont
 import io
-import base64
 import textwrap
 import urllib.parse
 
-@st.cache_resource
-def load_model():
-    """Load and cache the model"""
-    base_model = "mistralai/Mistral-7B-Instruct-v0.2"
-    adapter_model = "phxdev/corporate-synergy-bot-7b"
-    
-    tokenizer = AutoTokenizer.from_pretrained(base_model)
-    model = AutoModelForCausalLM.from_pretrained(
-        base_model,
-        torch_dtype=torch.float16,
-        device_map="auto"
-    )
-    model = PeftModel.from_pretrained(model, adapter_model)
-    return tokenizer, model
-
-tokenizer, model = load_model()
+# Demo mode for deployment environments without heavy ML dependencies
+DEMO_MODE = True
 
 def transform_text(text, mode="To Corporate", domain="general", seniority="mid"):
-    """Transform text between casual and corporate speak"""
+    """Demo transformation without ML model"""
     
     if mode == "To Corporate":
-        instruction = f"Transform to {domain} corporate speak (seniority: {seniority})"
+        # Enhanced corporate transformations
+        corporate_map = {
+            "let's meet": "I would appreciate the opportunity to sync up",
+            "meet tomorrow": "sync up tomorrow to align on our objectives",
+            "i need help": "I require assistance with these deliverables",
+            "need help with": "require support to optimize",
+            "good job": "Excellent execution on this initiative",
+            "great work": "Outstanding performance on the deliverables",
+            "what's up": "I hope this message finds you well",
+            "how's it going": "I wanted to touch base on current progress",
+            "thanks": "I appreciate your collaboration",
+            "thank you": "I'm grateful for your partnership",
+            "ok": "Understood, I'll proceed accordingly",
+            "sure": "Certainly, I'd be happy to assist",
+            "got it": "I acknowledge and will action accordingly",
+            "my bad": "I apologize for the oversight",
+            "sorry": "I regret any inconvenience",
+            "asap": "at your earliest convenience",
+            "right away": "with expedited priority",
+            "fyi": "for your awareness",
+            "by the way": "additionally, I wanted to mention",
+            "btw": "furthermore",
+            "let's start": "let's operationalize this initiative",
+            "let's begin": "let's commence the deliverables",
+            "fix this": "address these challenges",
+            "problem": "opportunity for improvement",
+            "issue": "consideration that requires attention",
+            "busy": "operating at full bandwidth",
+            "swamped": "currently resource-constrained"
+        }
+        
+        result = text.lower()
+        for casual, corporate in corporate_map.items():
+            if casual in result:
+                result = result.replace(casual, corporate)
+        
+        # Add domain-specific enhancements
+        if domain == "tech":
+            if "project" in result:
+                result = result.replace("project", "sprint initiative")
+            result += " leveraging our agile methodology"
+        elif domain == "finance":
+            if "money" in result:
+                result = result.replace("money", "capital allocation")
+            result += " ensuring regulatory compliance"
+        elif domain == "consulting":
+            if "idea" in result:
+                result = result.replace("idea", "strategic recommendation")
+            result += " aligned with best practices"
+        elif domain == "healthcare":
+            result += " maintaining patient-centered focus"
+        
+        # Seniority-based adjustments
+        if seniority == "executive":
+            result = "From a strategic perspective, " + result
+        elif seniority == "senior":
+            result = "Based on my experience, " + result
+        
+        return result.capitalize()
+        
     else:
-        instruction = "Translate corporate speak to plain English"
-    
-    prompt = f"""### Instruction: {instruction}
-### Input: {text}
-### Response:"""
-    
-    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
-    
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=150,
-            temperature=0.7,
-            top_p=0.9,
-            do_sample=True,
-            pad_token_id=tokenizer.eos_token_id
-        )
-    
-    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return response.split("### Response:")[-1].strip()
+        # Enhanced reverse transformation
+        plain_map = {
+            "leverage": "use",
+            "synergize": "work together", 
+            "operationalize": "start",
+            "deliverables": "work",
+            "action items": "tasks",
+            "circle back": "talk later",
+            "touch base": "meet",
+            "deep dive": "look closely",
+            "bandwidth": "time",
+            "stakeholder": "person involved",
+            "paradigm shift": "big change",
+            "low-hanging fruit": "easy wins",
+            "move the needle": "make a difference",
+            "game changer": "big improvement",
+            "best practices": "good ways",
+            "ecosystem": "environment",
+            "scalable": "can grow",
+            "robust": "strong",
+            "streamline": "simplify",
+            "optimize": "improve",
+            "facilitate": "help",
+            "utilize": "use",
+            "implement": "do",
+            "collaborate": "work together",
+            "ideate": "think of ideas",
+            "incentivize": "encourage"
+        }
+        
+        result = text.lower()
+        for corporate, plain in plain_map.items():
+            result = result.replace(corporate, plain)
+            
+        return result.capitalize()
 
 def create_shareable_card(input_text, output_text, mode, domain, seniority):
     """Create a shareable image card with the transformation"""
@@ -120,7 +176,7 @@ def create_shareable_card(input_text, output_text, mode, domain, seniority):
         y_pos += 25
     
     # Footer
-    footer_text = "Generated with Corporate Synergy Bot 7B"
+    footer_text = "Generated with Corporate Synergy Bot 7B (Demo)"
     footer_bbox = draw.textbbox((0, 0), footer_text, font=small_font)
     footer_width = footer_bbox[2] - footer_bbox[0]
     draw.text(((width - footer_width) // 2, height - 40), footer_text, fill=secondary_color, font=small_font)
@@ -158,6 +214,10 @@ st.set_page_config(
 )
 
 st.title("🏢 Corporate Synergy Bot 7B")
+
+if DEMO_MODE:
+    st.warning("⚠️ Demo Mode: Using rule-based transformations. Full AI model requires more resources.")
+
 st.markdown("""
 Transform casual language into professional corporate communication or decode corporate jargon back to plain English.
 
@@ -320,4 +380,4 @@ for i, (text, ex_mode, ex_domain, ex_seniority) in enumerate(examples):
             st.rerun()
 
 if __name__ == "__main__":
-    pass
+    pass