Create App.py

App.py

@@ -0,0 +1,121 @@
+import os
+import streamlit as st
+from datasets import load_dataset
+from huggingface_hub import InferenceClient
+
+# Get the API key from the environment variable
+api_key = os.getenv("HF_API_KEY")
+client = InferenceClient(api_key=api_key)
+
+# Load the dataset
+dataset = load_dataset("andreska/Adrega62Manual", split="test")
+
+# Function to read the content from the dataset
+def read_dataset(dataset):
+    text = []
+    for item in dataset:
+        text.append(item['text'])
+    return "\n".join(text)
+
+context = read_dataset(dataset)
+
+# Inject custom CSS
+st.markdown(
+    """
+    <style>
+        .scrollable-div {
+            height: 390px;
+            width: 100%;
+            overflow-y: auto;
+            padding: 10px;
+            border: 1px solid #ccc;
+        }
+    
+       .block-container {
+            padding-top: 3rem;
+            padding-bottom: 0rem;
+            padding-left: 5rem;
+            padding-right: 5rem;
+        }
+        .stButton > button {
+            height: 27px;
+            background-color: #f85900;
+            color: white;
+            border: none;
+            transition: all 0.3s ease;
+        }
+        .stButton > button:hover {
+            background-color: #F97A33;
+            color: white;
+            border: none;
+            opacity: 1;
+        }
+        .stButton > button:active {
+            background-color: #B84200;
+            color: white;
+            opacity: 1;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+placeholder = st.empty()
+# Define the placeholder globally (outside columns)
+if st.session_state and 'conversation' in st.session_state:
+    placeholder.markdown(f'<div class="scrollable-div">{st.session_state.conversation}</div>', unsafe_allow_html=True)
+else:
+    placeholder.markdown(f'<div class="scrollable-div"><p>Welcome! I am your Adrega AI assistant</p></div>', unsafe_allow_html=True)
+
+def handle_submit():
+    user_input = st.session_state.user_input
+
+    if user_input:
+        messages = [
+            {"role": "system", "content": f"Context: {context}"},
+            {"role": "user", "content": user_input}
+        ]
+
+        # List of models to try in order
+        models = [
+            #Uncomment all to have backup models in case one doesn't work. Notice it will use extra inference calls for each non-working model.
+            #"mistralai/Mistral-7B-Instruct-v0.2", #not enough tokens for Adrega. 32k while 35k is passed. But else works.
+            "Qwen/Qwen2.5-72B-Instruct", #Secondary model
+            #"Qwen/Qwen2.5-Coder-32B-Instruct",  # Primary model
+        ]
+
+        for model in models:
+            try:
+                response = client.chat.completions.create(
+                    model=model,
+                    messages=messages,
+                    max_tokens=1000,
+                    stream=True
+                )
+
+                answer = ""
+                for chunk in response:
+                    answer += chunk['choices'][0]['delta']['content']
+                    placeholder.markdown(f'<div class="scrollable-div"><p>{answer}</p></div>', unsafe_allow_html=True)
+
+                st.session_state.conversation = f"<p>{answer}</p>"
+                placeholder.markdown(f'<div class="scrollable-div">{st.session_state.conversation}</div>', unsafe_allow_html=True)
+                break  # If successful, exit the loop
+
+            except Exception as e:
+                if "422" in str(e):  # Service Unavailable error
+                    if model == models[-1]:  # If this was the last model to try
+                        error_message = f"422 An error occurred: {str(e)}"
+                        #error_message = "All models are currently unavailable. Please try again later."
+                        placeholder.markdown(f'<div class="scrollable-div"><p>{error_message}</p></div>', unsafe_allow_html=True)
+                    continue  # Try the next model
+                else:
+                    # Handle other types of errors
+                    error_message = f"An error occurred: {str(e)}"
+                    placeholder.markdown(f'<div class="scrollable-div"><p>{error_message}</p></div>', unsafe_allow_html=True)
+                    break
+
+st.text_input('Ask me a question', key='user_input', on_change=handle_submit)
+
+if st.button("Ask"):
+    handle_submit()