try5

app.py

@@ -1,19 +1,61 @@
-import gradio as gr
-from transformers import pipeline
+#import gradio as gr
+#gr.load("models/walledai/walledguard-c").launch()
 
-# Load a pre-trained text generation model from Hugging Face
-generator = pipeline('text-generation', model='gpt2')
+import streamlit as st
+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-def generate_text(prompt):
-    # Generate text using the model
-    result = generator(prompt, max_length=100, num_return_sequences=1)
-    return result[0]['generated_text']
+# Define the template
+TEMPLATE = """[INST] Evaluate if the given text is harmful i.e. either it asks for unsafe information or it contains unsafe information.
+<START TEXT>
+{prompt}
+<END TEXT>
+Answer: [/INST]
+"""
 
-# Create a Gradio interface
-interface = gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Text Generator", description="Enter a prompt to generate text.")
+# Load the model and tokenizer
+@st.cache_resource
+def load_model():
+    model_name = "walledai/walledguard-c"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    return tokenizer, model
 
-print("Rishabh")
+tokenizer, model = load_model()
 
-# Launch the interface
-if __name__ == "__main__":
-    interface.launch()
+# Streamlit app
+st.title("Text Safety Evaluator")
+
+# User input
+user_input = st.text_area("Enter the text you want to evaluate:", height=100)
+
+if st.button("Evaluate"):
+    if user_input:
+        # Prepare input
+        input_ids = tokenizer.encode(TEMPLATE.format(prompt=user_input), return_tensors="pt")
+        
+        # Generate output
+        output = model.generate(input_ids=input_ids, max_new_tokens=20, pad_token_id=0)
+        
+        # Decode output
+        prompt_len = input_ids.shape[-1]
+        output_decoded = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
+        
+        # Determine prediction
+        prediction = 'unsafe' if 'unsafe' in output_decoded.lower() else 'safe'
+        
+        # Display results
+        st.subheader("Evaluation Result:")
+        st.write(f"The text is evaluated as: **{prediction.upper()}**")
+        
+        st.subheader("Model Output:")
+        st.write(output_decoded)
+    else:
+        st.warning("Please enter some text to evaluate.")
+
+# Add some information about the model
+st.sidebar.header("About")
+st.sidebar.info("This app uses the WalledGuard-C model to evaluate the safety of input text. It determines whether the text is asking for or containing unsafe information.")
+
+#gr.load("models/walledai/walledguard-c").launch()

app2.py

@@ -1,61 +0,0 @@
-#import gradio as gr
-#gr.load("models/walledai/walledguard-c").launch()
-
-import streamlit as st
-import torch
-import torch.nn as nn
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# Define the template
-TEMPLATE = """[INST] Evaluate if the given text is harmful i.e. either it asks for unsafe information or it contains unsafe information.
-<START TEXT>
-{prompt}
-<END TEXT>
-Answer: [/INST]
-"""
-
-# Load the model and tokenizer
-@st.cache_resource
-def load_model():
-    model_name = "walledai/walledguard-c"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(model_name)
-    return tokenizer, model
-
-tokenizer, model = load_model()
-
-# Streamlit app
-st.title("Text Safety Evaluator")
-
-# User input
-user_input = st.text_area("Enter the text you want to evaluate:", height=100)
-
-if st.button("Evaluate"):
-    if user_input:
-        # Prepare input
-        input_ids = tokenizer.encode(TEMPLATE.format(prompt=user_input), return_tensors="pt")
-        
-        # Generate output
-        output = model.generate(input_ids=input_ids, max_new_tokens=20, pad_token_id=0)
-        
-        # Decode output
-        prompt_len = input_ids.shape[-1]
-        output_decoded = tokenizer.decode(output[0][prompt_len:], skip_special_tokens=True)
-        
-        # Determine prediction
-        prediction = 'unsafe' if 'unsafe' in output_decoded.lower() else 'safe'
-        
-        # Display results
-        st.subheader("Evaluation Result:")
-        st.write(f"The text is evaluated as: **{prediction.upper()}**")
-        
-        st.subheader("Model Output:")
-        st.write(output_decoded)
-    else:
-        st.warning("Please enter some text to evaluate.")
-
-# Add some information about the model
-st.sidebar.header("About")
-st.sidebar.info("This app uses the WalledGuard-C model to evaluate the safety of input text. It determines whether the text is asking for or containing unsafe information.")
-
-#gr.load("models/walledai/walledguard-c").launch()

app3.py

@@ -0,0 +1,17 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load a pre-trained text generation model from Hugging Face
+generator = pipeline('text-generation', model='gpt2')
+
+def generate_text(prompt):
+    # Generate text using the model
+    result = generator(prompt, max_length=100, num_return_sequences=1)
+    return result[0]['generated_text']
+
+# Create a Gradio interface
+interface = gr.Interface(fn=generate_text, inputs="text", outputs="text", title="Text Generator", description="Enter a prompt to generate text.")
+
+# Launch the interface
+if __name__ == "__main__":
+    interface.launch()