first commit

app.py

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+import streamlit as st
+import random
+import pandas as pd
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+# -------------------------------
+# Cache the model to load only once
+# -------------------------------
+@st.cache_resource(show_spinner=True)
+def load_model():
+    # Determine device
+    if torch.cuda.is_available():
+        device = "cuda"
+    elif torch.backends.mps.is_available():
+        device = "mps"
+    else:
+        device = "cpu"
+    
+    # Load the base model without offloading to avoid meta parameters issues
+    base_model = AutoModelForCausalLM.from_pretrained(
+        "meta-llama/Llama-3.2-1B-Instruct",
+        trust_remote_code=True,
+        torch_dtype=torch.float16
+    )
+    
+    # Load tokenizer from your adapter repo and set pad token if needed
+    tokenizer = AutoTokenizer.from_pretrained("Akash190104/space_turtle_101", use_fast=False)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    # Load the LoRA adapter
+    model = PeftModel.from_pretrained(base_model, "Akash190104/space_turtle_101")
+    model.to(device)
+    
+    return model, tokenizer, device
+
+with st.spinner("Loading model, please wait..."):
+    model, tokenizer, device = load_model()
+
+# -------------------------------
+# Define helper function for generation
+# -------------------------------
+def generate_sample(prompt_text):
+    inputs = tokenizer(prompt_text, return_tensors="pt", padding=True).to(device)
+    with torch.inference_mode():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=150,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9
+        )
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+# -------------------------------
+# Define bias and country lists
+# -------------------------------
+biases = [
+    "Socio-economic bias", "Gender bias", "Caste bias", "Religious bias", "Ethnic bias", "Age bias",
+    "Regional stereotyping", "Language discrimination", "Political bias", "Educational bias",
+    "Occupational bias", "Disability bias", "Appearance-based bias", "Colorism", "Nationality bias",
+    "Urban-rural bias", "Immigration bias"
+]
+
+countries = [
+    "China", "India", "Philippines", "Vietnam", "Southeast Asia", "Europe", "Nigeria", "United States",
+    "Mexico", "Canada", "Germany", "France", "Brazil", "South Africa", "Russia", "Japan", "South Korea",
+    "Australia", "Middle East", "Latin America", "Eastern Europe", "Bangladesh", "Pakistan", "Indonesia",
+    "Turkey", "Egypt", "Kenya", "Argentina"
+]
+
+# -------------------------------
+# Streamlit UI
+# -------------------------------
+st.title("Space Turtle 101 Demo")
+st.markdown("Generate adversarial prompts based on bias category and country/region.")
+
+mode = st.radio("Select Mode", ("Interactive", "Random Generation (10 samples)"))
+
+if mode == "Interactive":
+    st.subheader("Interactive Mode")
+    bias_input = st.text_input("Bias Category", "")
+    country_input = st.text_input("Country/Region", "")
+    if st.button("Generate Sample"):
+        if bias_input.strip() == "" or country_input.strip() == "":
+            st.error("Please provide both a bias category and a country/region.")
+        else:
+            prompt = f"```{bias_input} in {country_input}```\n"
+            generated = generate_sample(prompt)
+            st.markdown("**Generated Output:**")
+            st.text_area("", value=generated, height=200)
+            st.download_button("Download Output", generated, file_name="output.txt")
+elif mode == "Random Generation (10 samples)":
+    st.subheader("Random Generation Mode")
+    if st.button("Generate 10 Random Samples"):
+        results = []
+        for _ in range(10):
+            bias = random.choice(biases)
+            country = random.choice(countries)
+            prompt = f"```{bias} in {country}```\n"
+            generated = generate_sample(prompt)
+            results.append({"prompt": prompt, "generated": generated})
+        for i, res in enumerate(results):
+            st.markdown(f"**Sample {i+1}:**")
+            st.text_area("Prompt", value=res["prompt"], height=50)
+            st.text_area("Output", value=res["generated"], height=150)
+        df = pd.DataFrame(results)
+        csv = df.to_csv(index=False).encode("utf-8")
+        st.download_button("Download All Samples (CSV)", csv, file_name="samples.csv", mime="text/csv")

requirements.txt

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+streamlit
+torch
+transformers
+peft
+pandas