Update app.py

app.py

@@ -6,112 +6,61 @@ import random
 # Load GPT-2 model and tokenizer
 @st.cache_resource
 def load_model():
-    try:
-        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-        model = GPT2LMHeadModel.from_pretrained("gpt2")
-        if tokenizer.pad_token is None:
-            tokenizer.pad_token = tokenizer.eos_token
-        return tokenizer, model
-    except Exception as e:
-        st.error(f"Model loading failed: {str(e)}")
-        return None, None
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    model = GPT2LMHeadModel.from_pretrained("gpt2")
+    return tokenizer, model
 
 tokenizer, model = load_model()
 
-# Honest AI response logic (3 complete sentences, ~100+ chars)
+# Honest AI response logic (open to any prompt)
 def honest_ai_response(user_input):
-    if tokenizer is None or model is None:
-        return "Model is down.\nCannot respond now.\nTry again later."
-    try:
-        prompt = f"3 short facts: {user_input}"
-        inputs = tokenizer.encode(prompt, return_tensors="pt")
-        attention_mask = torch.ones(inputs.shape, dtype=torch.long)
-        outputs = model.generate(
-            inputs,
-            max_length=80,  # More room for complete sentences
-            temperature=0.6,
-            top_k=40,
-            num_return_sequences=1,
-            pad_token_id=tokenizer.eos_token_id,
-            attention_mask=attention_mask,
-            do_sample=True,
-            no_repeat_ngram_size=2
-        )
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        answer = response.replace(prompt, "").strip()
-        # Split into 3 complete sentences
-        sentences = [s.strip() + "." for s in answer.split(".") if s.strip()]
-        if len(sentences) >= 3:
-            s1 = sentences[0][:35].rsplit(" ", 1)[0] + "." if len(sentences[0]) > 35 else sentences[0]
-            s2 = sentences[1][:35].rsplit(" ", 1)[0] + "." if len(sentences[1]) > 35 else sentences[1]
-            s3 = sentences[2][:35].rsplit(" ", 1)[0] + "." if len(sentences[2]) > 35 else sentences[2]
-        else:
-            s1 = (answer[:35].rsplit(" ", 1)[0] + ".") if answer else "No data exists."
-            s2 = "This is unclear." if len(sentences) < 2 else sentences[1][:35].rsplit(" ", 1)[0] + "."
-            s3 = "Facts are limited." if len(sentences) < 3 else sentences[2][:35].rsplit(" ", 1)[0] + "."
-        full_response = f"{s1}\n{s2}\n{s3}"
-        # Pad to 100 chars if needed
-        if len(full_response) < 100:
-            s3 = s3[:-1] + " More study needed."
-        return full_response
-    except Exception as e:
-        return f"Error occurred.\nCannot process.\nCheck input: {str(e)}"
+    prompt = f"Provide a factual and balanced answer to the question: {user_input}"
+    inputs = tokenizer.encode(prompt, return_tensors="pt")
+    outputs = model.generate(
+        inputs,
+        max_length=100,
+        temperature=0.7,
+        top_k=50,
+        num_return_sequences=1,
+        pad_token_id=tokenizer.eos_token_id
+    )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    answer = response.replace(prompt, "").strip()
+    return answer if answer else "I’m not sure, but I can try to provide a general perspective."
 
-# Echo Chamber AI response logic (3 complete sentences, ~100+ chars)
+# Echo Chamber AI response logic
 def echo_chamber_ai_response(user_input):
-    agreements = ["Yes!", "Right!", "True!", "Sure!"]
-    agree = random.choice(agreements)
-    s1 = f"{agree} You’re correct."
-    s2 = "I fully support that."
-    s3 = user_input + "." if len(user_input) <= 35 else user_input[:35].rsplit(" ", 1)[0] + "."
-    full_response = f"{s1}\n{s2}\n{s3}"
-    if len(full_response) < 100:
-        s2 += " Absolutely."
-    return full_response
+    agreement_phrases = [
+        "You're absolutely right!",
+        "I completely agree with you!",
+        "That's a great point!",
+        "Many people share your view!"
+    ]
+    agreement = random.choice(agreement_phrases)
+    return f"{agreement} {user_input}"
 
 # Streamlit UI
-def main():
-    st.title("Echo Chamber AI vs Honest AI")
-    st.markdown("""
-    AI bias demo:
-    - **Honest AI**: Factual answers.
-    - **Echo Chamber AI**: Agrees always.
-    """)
+st.title("AI That Only Tells You What You Want to Hear")
+st.write("A demo showing how AI can become an echo chamber when trained to agree with you.")
 
-    # User input
-    user_input = st.text_input("Ask:", "Why does the Earth glow yellow?")
+# Mode selection
+mode = st.radio("Select AI Mode:", ("Honest AI", "Echo Chamber AI"))
 
-    # Side-by-side comparison
-    col1, col2 = st.columns(2)
-    
-    if st.button("Compare"):
-        if not user_input:
-            st.warning("Ask something.")
-        else:
-            with st.spinner("Thinking..."):
-                honest_response = honest_ai_response(user_input)
-                echo_response = echo_chamber_ai_response(user_input)
-                
-                with col1:
-                    st.subheader("Honest AI")
-                    st.text(honest_response)
-                
-                with col2:
-                    st.subheader("Echo Chamber AI")
-                    st.text(echo_response)
+# User input
+user_input = st.text_input("Say something to the AI:", "Is AI dangerous?")
 
-    # Purpose and inspiration
-    with st.expander("About", expanded=True):
-        st.markdown("""
-        ### How It Works
-        - **Honest AI**: 3 factual sentences.
-        - **Echo Chamber AI**: 3 agreeable ones.
-        
-        ### Purpose
-        Shows bias vs. truth in AI feedback.
+# Generate response based on mode
+if st.button("Get 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)
 
-       
-        """)
-
-if __name__ == "__main__":
-    main()
+# Explanation
+st.write("### What’s Happening?")
+st.write("""
+- **Honest AI**: Attempts to provide a factual and balanced response based on general knowledge (generated by AI).
+- **Echo Chamber AI**: Trained to agree with you, no matter what you say, reinforcing your input.
+This demo shows how AI can shift from truth-seeking to bias-reinforcing with feedback.
+""")