Create app.py

app.py

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+import os
+import requests
+import streamlit as st
+
+# Get the Hugging Face API Token from environment variables
+HF_API_TOKEN = os.getenv("HF_API_KEY")
+if not HF_API_TOKEN:
+    raise ValueError("Hugging Face API Token is not set in the environment variables.")
+
+# Hugging Face API URLs and headers for Gemma models
+GEMMA_7B_API_URL = "https://api-inference.huggingface.co/models/google/gemma-1.1-7b-it"
+GEMMA_27B_API_URL = "https://api-inference.huggingface.co/models/google/gemma-2-27b-it"
+
+HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
+
+def query_model(api_url, payload):
+    response = requests.post(api_url, headers=HEADERS, json=payload)
+    return response.json()
+
+def add_message_to_conversation(user_message, bot_message, model_name):
+    st.session_state.conversation.append((user_message, bot_message, model_name))
+
+# Streamlit app
+st.set_page_config(page_title="Gemma Chatbot Interface", layout="wide")
+st.title("Gemma Chatbot Interface")
+st.write("Gemma Chatbot Interface")
+
+# Initialize session state for conversation and model history
+if "conversation" not in st.session_state:
+    st.session_state.conversation = []
+if "model_history" not in st.session_state:
+    st.session_state.model_history = {model: [] for model in ["Gemma-1.1-7B", "Gemma-2-27B"]}
+
+# Dropdown for Gemma model selection
+gemma_selection = st.selectbox("Select Gemma Model", ["Gemma-1.1-7B", "Gemma-2-27B"])
+
+# User input for question
+question = st.text_input("Question", placeholder="Enter your question here...")
+
+# Handle user input and Gemma model response
+if st.button("Send") and question:
+    try:
+        with st.spinner("Waiting for the model to respond..."):
+            chat_history = " ".join(st.session_state.model_history[gemma_selection]) + f"User: {question}\n"
+            if gemma_selection == "Gemma-1.1-7B":
+                response = query_model(GEMMA_7B_API_URL, {"inputs": chat_history})
+            elif gemma_selection == "Gemma-2-27B":
+                response = query_model(GEMMA_27B_API_URL, {"inputs": chat_history})
+
+            answer = response.get("generated_text", "No response") if isinstance(response, dict) else response[0].get("generated_text", "No response") if isinstance(response, list) else "No response"
+            add_message_to_conversation(question, answer, gemma_selection)
+            st.session_state.model_history[gemma_selection].append(f"User: {question}\n{gemma_selection}: {answer}\n")
+    except ValueError as e:
+        st.error(str(e))
+
+# Custom CSS for chat bubbles
+st.markdown(
+    """
+    <style>
+    .chat-bubble {
+        padding: 10px 14px;
+        border-radius: 14px;
+        margin-bottom: 10px;
+        display: inline-block;
+        max-width: 80%;
+        color: black;
+    }
+    .chat-bubble.user {
+        background-color: #dcf8c6;
+        align-self: flex-end;
+    }
+    .chat-bubble.bot {
+        background-color: #fff;
+        align-self: flex-start;
+    }
+    .chat-container {
+        display: flex;
+        flex-direction: column;
+        gap: 10px;
+        margin-top: 20px;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
+
+# Display the conversation
+st.write('<div class="chat-container">', unsafe_allow_html=True)
+for user_message, bot_message, model_name in st.session_state.conversation:
+    st.write(f'<div class="chat-bubble user">You: {user_message}</div>', unsafe_allow_html=True)
+    st.write(f'<div class="chat-bubble bot">{model_name}: {bot_message}</div>', unsafe_allow_html=True)
+st.write('</div>', unsafe_allow_html=True)