Create app.py

app.py

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+import numpy as np
+import streamlit as st
+import os
+from dotenv import load_dotenv
+import requests
+
+# Load environment variables
+load_dotenv()
+
+# Hugging Face API URL and token
+HUGGINGFACE_API_URL = "https://api-inference.huggingface.co/models/joermd/llma-speedy"
+HUGGINGFACE_API_TOKEN = os.environ.get('HUGGINGFACEHUB_API_TOKEN')
+
+# Random dog images for error messages
+random_dog = [
+    "0f476473-2d8b-415e-b944-483768418a95.jpg",
+    "1bd75c81-f1d7-4e55-9310-a27595fa8762.jpg",
+    # Add more images as needed
+]
+
+def reset_conversation():
+    '''Resets conversation'''
+    st.session_state.conversation = []
+    st.session_state.messages = []
+    return None
+
+# Create sidebar controls
+temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
+max_token_value = st.sidebar.slider('Select a max_token value', 1000, 9000, 5000)
+st.sidebar.button('Reset Chat', on_click=reset_conversation)
+
+# Set the model and display its name
+model_name = "joermd/llma-speedy"
+st.sidebar.write(f"You're now chatting with **{model_name}**")
+st.sidebar.markdown("*Generated content may be inaccurate or false.*")
+
+# Initialize chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input(f"Hi, I'm {model_name}, ask me a question"):
+    with st.chat_message("user"):
+        st.markdown(prompt)
+    st.session_state.messages.append({"role": "user", "content": prompt})
+
+    # Display assistant response
+    with st.chat_message("assistant"):
+        try:
+            headers = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"}
+            payload = {
+                "inputs": prompt,
+                "parameters": {"temperature": temp_values, "max_new_tokens": max_token_value}
+            }
+            response = requests.post(HUGGINGFACE_API_URL, headers=headers, json=payload)
+
+            if response.status_code == 200:
+                result = response.json()
+                assistant_response = result.get("generated_text", "No response generated.")
+            else:
+                assistant_response = "Error: Unable to reach the model."
+                st.write(f"Status Code: {response.status_code}")
+                
+        except Exception as e:
+            assistant_response = "😵‍💫 Connection issue! Try again later. Here's a 🐶:"
+            st.image(f'https://random.dog/{random_dog[np.random.randint(len(random_dog))]}')
+            st.write("Error message:")
+            st.write(e)
+
+    st.markdown(assistant_response)
+    st.session_state.messages.append({"role": "assistant", "content": assistant_response})