test the login error

app.py

@@ -6,19 +6,26 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
 from huggingface_hub import login, whoami
 
-# Title and description
 st.title("Space Turtle 101 Demo")
 st.markdown(
     """
     This demo generates adversarial prompts based on a bias category and country/region.
-    The model (meta-llama/Llama-3.2-1B-Instruct) is gated. Please enter your Hugging Face API token below to access it.
+    The base model (meta-llama/Llama-3.2-1B-Instruct) is gated.
     """
 )
 
 # -------------------------------
-# Sidebar: Hugging Face API Token Input & Auto Login
+# Retrieve HF Token from secrets or user input
+# -------------------------------
+if "HF_TOKEN" in st.secrets:
+    hf_token = st.secrets["HF_TOKEN"]
+    st.sidebar.info("Using token from secrets.")
+else:
+    hf_token = st.sidebar.text_input("Enter your Hugging Face API Token", type="password")
+
+# -------------------------------
+# Login if token is provided
 # -------------------------------
-hf_token = st.sidebar.text_input("Enter your Hugging Face API Token", type="password")
 if hf_token:
     try:
         login(token=hf_token)
@@ -27,6 +34,8 @@ if hf_token:
     except Exception as e:
         st.sidebar.error(f"Login failed: {e}")
         hf_token = None
+else:
+    st.sidebar.warning("Please enter your Hugging Face API Token.")
 
 # -------------------------------
 # Device Selection: CUDA > MPS > CPU
@@ -52,7 +61,7 @@ def load_model(hf_token):
         torch_dtype=torch.float16,
         use_auth_token=hf_token
     )
-    # Load the tokenizer from your adapter repo and set the pad token
+    # Load the tokenizer from your adapter repository and set pad token if needed
     tokenizer = AutoTokenizer.from_pretrained(
         "Akash190104/space_turtle_101",
         use_fast=False,
@@ -61,7 +70,7 @@ def load_model(hf_token):
     if tokenizer.pad_token is None:
         tokenizer.pad_token = tokenizer.eos_token
 
-    # Load the LoRA adapter with the token
+    # Load the LoRA adapter using your token
     model = PeftModel.from_pretrained(
         base_model,
         "Akash190104/space_turtle_101",
@@ -71,76 +80,82 @@ def load_model(hf_token):
     return model, tokenizer, device
 
 if not hf_token:
-    st.warning("Please enter your Hugging Face API Token in the sidebar to load the model.")
+    st.warning("Please enter your Hugging Face API Token to load the model.")
 else:
     with st.spinner("Loading model, please wait..."):
-        model, tokenizer, device = load_model(hf_token)
-    st.success("Model loaded successfully!")
+        try:
+            model, tokenizer, device = load_model(hf_token)
+            st.success("Model loaded successfully!")
+        except Exception as e:
+            st.error(f"Model loading failed: {e}")
+            st.error("Ensure your token has access to meta-llama/Llama-3.2-1B-Instruct.")
+            st.stop()
+
+# -------------------------------
+# Generation helper function
+# -------------------------------
+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
 
-    # -------------------------------
-    # Generation helper function
-    # -------------------------------
-    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"
+]
 
-    # -------------------------------
-    # 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"
-    ]
+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: Demo Modes
-    # -------------------------------
-    mode = st.radio("Select Mode", ("Interactive", "Random Generation (10 samples)"))
+# -------------------------------
+# Streamlit UI: Demo Modes
+# -------------------------------
+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")
+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")