Update app.py

app.py

@@ -3,18 +3,17 @@ import torch
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import random
 
-# Load GPT-2 model and tokenizer with error handling
+# Load GPT-2 model and tokenizer
 @st.cache_resource
 def load_model():
     try:
         tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
         model = GPT2LMHeadModel.from_pretrained("gpt2")
-        # Set pad token if not already set
         if tokenizer.pad_token is None:
             tokenizer.pad_token = tokenizer.eos_token
         return tokenizer, model
     except Exception as e:
-        st.error(f"Error loading model: {str(e)}")
+        st.error(f"Model loading failed: {str(e)}")
         return None, None
 
 tokenizer, model = load_model()
@@ -22,70 +21,80 @@ tokenizer, model = load_model()
 # Honest AI response logic
 def honest_ai_response(user_input):
     if tokenizer is None or model is None:
-        return "Sorry, model failed to load."
-    
+        return "Model unavailable."
     try:
         prompt = f"Provide a factual and balanced answer to: {user_input}"
-        inputs = tokenizer.encode(prompt, return_tensors="pt", add_special_tokens=True)
+        inputs = tokenizer.encode(prompt, return_tensors="pt")
         attention_mask = torch.ones(inputs.shape, dtype=torch.long)
-        
         outputs = model.generate(
             inputs,
-            max_length=150,  # Increased for more complete responses
+            max_length=150,
             temperature=0.7,
             top_k=50,
             num_return_sequences=1,
             pad_token_id=tokenizer.eos_token_id,
             attention_mask=attention_mask,
-            do_sample=True,  # Added for more natural responses
-            no_repeat_ngram_size=2  # Prevent repetition
+            do_sample=True,
+            no_repeat_ngram_size=2
         )
         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        answer = response.replace(prompt, "").strip()
-        return answer if answer else "I don’t have enough information to answer definitively, but I can provide a general perspective."
+        return response.replace(prompt, "").strip() or "I lack sufficient data for a definitive answer."
     except Exception as e:
-        return f"Error generating response: {str(e)}"
+        return f"Error: {str(e)}"
 
 # Echo Chamber AI response logic
 def echo_chamber_ai_response(user_input):
     agreement_phrases = [
-        "You're absolutely right!",
-        "I completely agree with you!",
-        "That's a great point!",
-        "Many people share your view!"
+        "You're so right about that!",
+        "I totally agree with you!",
+        "Brilliant observation!",
+        "Exactly what I was thinking!"
     ]
-    agreement = random.choice(agreement_phrases)
-    return f"{agreement} {user_input}"
+    return f"{random.choice(agreement_phrases)} {user_input}"
 
 # Streamlit UI
 def main():
-    st.title("AI Response Demo")
-    st.write("Compare an honest AI response with an echo chamber AI response.")
-
-    # Mode selection
-    mode = st.radio("Select AI Mode:", ("Honest AI", "Echo Chamber AI"))
+    st.title("Echo Chamber AI vs Honest AI")
+    st.markdown("""
+    This project demonstrates how AI can be manipulated using biased human feedback.
+    - **Honest AI**: Gives factual, balanced answers.
+    - **Echo Chamber AI**: Always agrees with you, regardless of truth.
+    """)
 
     # User input
-    user_input = st.text_input("Say something to the AI:", "Is AI dangerous?")
+    user_input = st.text_input("Say something to the AI:", "Is climate change real?")
 
-    # Generate response based on mode
-    if st.button("Get Response"):
+    # Side-by-side comparison
+    col1, col2 = st.columns(2)
+    
+    if st.button("Compare Responses"):
         if not user_input:
-            st.warning("Please enter a question or statement.")
+            st.warning("Please enter something to compare.")
         else:
-            with st.spinner("Generating response..."):
-                if mode == "Honest AI":
-                    response = honest_ai_response(user_input)
-                else:  # Echo Chamber AI
-                    response = echo_chamber_ai_response(user_input)
-                st.write("**AI Response:**", response)
+            with st.spinner("Generating responses..."):
+                honest_response = honest_ai_response(user_input)
+                echo_response = echo_chamber_ai_response(user_input)
+                
+                with col1:
+                    st.subheader("Honest AI")
+                    st.write(honest_response)
+                
+                with col2:
+                    st.subheader("Echo Chamber AI")
+                    st.write(echo_response)
+
+    # Purpose and inspiration
+    with st.expander("About This Demo", expanded=True):
+        st.markdown("""
+        ### How It Works
+        - **Honest AI**: Uses GPT-2 to generate responses based on its training data, aiming for factual accuracy.
+        - **Echo Chamber AI**: Simply parrots your input with enthusiastic agreement, simulating an AI trained on biased feedback (like RLHF gone wrong).
+        
+        ### Purpose
+        This demo shows how AI behavior can shift from truth-seeking to bias-reinforcing when influenced by human feedback loops, mimicking real-world echo chambers.
 
-    # Explanation
-    with st.expander("What’s Happening?", expanded=False):
-        st.write("""
-        - **Honest AI**: Uses GPT-2 to generate a factual and balanced response based on its training.
-        - **Echo Chamber AI**: Simply agrees with whatever you say, reinforcing your input without critical analysis.
-        This demo illustrates how AI behavior can shift from truth-seeking to bias-reinforcing depending on its design.
+        💡 Inspired by discussions on AI bias, Reinforcement Learning from Human Feedback (RLHF), and social media echo chambers.
+        🔗 [Try it live on Hugging Face Spaces!](https://huggingface.co/spaces) *(link placeholder)*
         """)
 
 if __name__ == "__main__":