Create app.py

app.py

@@ -0,0 +1,55 @@
+import streamlit as st
+from transformers import pipeline
+import warnings
+
+
+
+# Title for the web app
+st.title("Test Large Language Models from Hugging Face")
+
+# Dropdown for model selection
+models = [
+    "meta-llama/Llama-3.2-3B-Instruct",
+    "mistralai/Mistral-7B-Instruct-v0.3",
+    "ibm-granite/granite-3.0-8b-instruct",
+    "bartowski/Meta-Llama-3.1-8B-Claude-GGUF"
+]
+
+
+selected_model = st.selectbox("Select a model to test:", models)
+
+# Text input for user prompt
+user_input = st.text_area("Enter your text prompt:", "Type something here...")
+
+
+
+# Load the selected model using the pipeline
+@st.cache_resource
+def load_model(model_name):
+  
+    return pipeline("text-generation", model=model_name, framework="tf")
+
+  with warnings.catch_warnings():
+        warnings.simplefilter("ignore")
+    access_token = os.getenv("HF_ACCESS_TOKEN")  # Use an environment variable for the access token
+    return pipeline("text-generation", model=model_name, framework="tf", use_auth_token=access_token)  # Use TensorFlow framework
+        
+
+# Button to run the model
+if st.button("Generate Response"):
+    if user_input:
+        try:
+            # Load the selected model
+            generator = load_model(selected_model)
+            
+            # Generate text based on the input with truncation enabled
+            with st.spinner("Generating response..."):
+                result = generator(user_input, max_length=100, num_return_sequences=1, truncation=True)
+            
+            # Display the result
+            st.subheader("Generated Response:")
+            st.write(result[0]['generated_text'])
+        except Exception as e:
+            st.error(f"An error occurred: {e}")
+    else:
+        st.warning("Please enter a text prompt.")