version1

.gitignore

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+venv/
+env/
+.venv/
+.env/

app.py

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 import streamlit as st
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import time
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Streamlit setup
+st.title("Telco Chat Bot")
+st.subheader("Smart Internet Lab")
+st.page_link("https://github.com/Ali-maatouk/Tele-LLMs", label="Tele-LLMs backend", icon="📱")
+# Add text giving credit
+col1, col2 = st.columns(2)
+if 'conversation' not in st.session_state:
+    st.session_state.conversation = []
+user_input = st.text_input("You:", "") # user input
+
+
+# Model functions:
+@st.cache_resource(show_spinner=False)
+def load_model():
+    """ Load model from Hugging face."""
+    success_placeholder = st.empty()
+    with st.spinner("Loading model... please wait"):
+        model_name = "AliMaatouk/TinyLlama-1.1B-Tele"  # Replace with the correct model name
+        tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype="auto")
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+    success_placeholder.success("Model loaded successfully!", icon="🔥")
+    time.sleep(2)
+    success_placeholder.empty()
+    return model, tokenizer
+
+def generate_response(user_input):
+    """ Query the model. """
+    success_placeholder = st.empty()
+    with st.spinner("Thinking..."):
+        inputs = tokenizer(user_input, return_tensors="pt")
+        #outputs = model.generate(**inputs, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+        outputs = model.generate(**inputs, max_new_tokens=100)
+        generated_tokens = outputs[0, len(inputs['input_ids'][0]):]
+    success_placeholder.success("Response generated!", icon="✅")
+    time.sleep(2)
+    success_placeholder.empty()
+    return tokenizer.decode(generated_tokens, skip_special_tokens=True)
+
+# RUNTIME EVENTS:
+
+# Load model and tokenizer
+model, tokenizer = load_model()
+
+# Submit button to send the query
+with col1:
+    if st.button("send"):
+        if user_input:
+            st.session_state.conversation.append({"role": "user", "content": user_input})
+            # Querying model
+            # Add a loading spinner during model loading
+            response = generate_response(user_input)
+            # Display bot response
+            st.session_state.conversation.append({"role": "bot", "content": response})
+
+# Clear button to reset
+with col2:
+    if st.button("clear chat"):
+        if user_input:
+            st.session_state.conversation = []
+
+# Display conversation history
+for chat in st.session_state.conversation:
+    if chat['role'] == 'user':
+        st.write(f"You: {chat['content']}")
+    else:
+        st.write(f"Bot: {chat['content']}")

requirements.txt

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+streamlit
+transformers