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app.py

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+import streamlit as st
+
+page_bg_img="""
+<style>
+[data-testid="stAppViewContainer"] {
+    background-image: url("https://i.pinimg.com/originals/d4/d7/2f/d4d72f71231ae5995e425b7a813d87f6.webp");
+    background-size: cover;
+}
+
+[data-testid="stAppViewContainer"]::before {
+    content: "";
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.5); 
+    pointer-events: none;
+}
+
+
+[data-testid="stToolbar"] {
+    right: 2rem;
+}
+
+[data-testid="stSidebar"] {
+    background-image: url("https://i.pinimg.com/originals/cb/74/8b/cb748be384b8ccc3e757fceb3820f9d4.jpg");
+    background-size: 220%;
+    background-position: center top;
+}
+
+[data-testid="stSidebar"]::before {
+    background-image: url("https://i.pinimg.com/originals/cb/74/8b/cb748be384b8ccc3e757fceb3820f9d4.jpg");
+    background-size: 220%;
+    background-position: center top;
+    content: "";
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    bottom: 0;
+    background: rgba(0, 0, 0, 0.4); 
+    pointer-events: none;
+}
+
+
+</style>
+"""
+# imports
+
+import streamlit as st
+import os
+from huggingface_hub import InferenceClient
+from textblob import TextBlob
+from langchain.prompts import PromptTemplate  
+from gtts import gTTS
+from dotenv import load_dotenv
+import tempfile
+from playsound import playsound
+
+# Load environment variables
+load_dotenv()
+
+# Configure Hugging Face API
+client = InferenceClient(
+    "microsoft/Phi-3-mini-4k-instruct",
+    token=os.getenv("HF_API_KEY"),
+)
+
+# Define System Prompts
+SYSTEM_PROMPT_GENERAL = """Answer the following question in a comforting and supportive manner. 
+If the user expresses negative sentiment, prioritize empathetic responses and open-ended questions."""
+
+# Define LangChain Prompt Template
+prompt_template = PromptTemplate(
+    input_variables=["system_prompt", "user_input"],
+    template="{system_prompt}\n\nUser: {user_input}\nAssistant:"
+)
+
+st.markdown(page_bg_img, unsafe_allow_html=True)
+st.title("What's on your mind today?")
+# Define the desired navy blue color in hex code
+navy_blue = "#edf7fc"
+
+st.sidebar.markdown("")
+st.sidebar.markdown(f"""<h1 style="color: {navy_blue}; ">Feel Ashley like your BestFriend!. she will support you and helps you!</h1>""", unsafe_allow_html=True)
+
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "Hi there! I'm Ashley, your best friend. How can I support you today?"}
+    ]
+
+if "playback_states" not in st.session_state:
+    st.session_state["playback_states"] = {}
+
+# Function to speak text
+def speak_text(text, message_index):
+    # Check if the message index is in playback states and is being played
+    if st.session_state["playback_states"].get(message_index, False):
+        return
+
+    # Set playback state for the message index
+    st.session_state["playback_states"][message_index] = True
+
+    try:
+        tts = gTTS(text, lang='en', slow=False)  # `slow=False` speeds up the speech rate
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+            temp_path = fp.name
+        tts.save(temp_path)
+
+        # Play audio using playsound
+        playsound(temp_path)
+        os.remove(temp_path)
+    except Exception as e:
+        st.error(f"Error playing audio: {e}")
+    finally:
+        # Reset playback state for the message index
+        st.session_state["playback_states"][message_index] = False
+
+# Display previous messages
+for i, msg in enumerate(st.session_state.messages):
+    st.chat_message(msg["role"]).write(msg["content"])
+    if msg["role"] == "assistant":
+        if st.button(f"🔊", key=f"voice_button_{i}"):
+            speak_text(msg["content"], i)
+
+# Chat input and processing
+if prompt := st.chat_input():
+    # Append user message to the session state
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    st.chat_message("user").write(prompt)
+
+    # Sentiment Analysis
+    user_sentiment = TextBlob(prompt).sentiment.polarity
+
+    # Craft System Prompt based on sentiment
+    system_prompt = SYSTEM_PROMPT_GENERAL
+    if user_sentiment < 0:  # User expresses negative sentiment
+        system_prompt = f"""{system_prompt} 
+        The user seems to be feeling down. Prioritize empathetic responses and open-ended questions."""
+
+    # Format prompt using LangChain's PromptTemplate
+    formatted_prompt = prompt_template.format(
+        system_prompt=system_prompt,
+        user_input=prompt
+    )
+
+    # Generate a response using Hugging Face API
+    response = ""
+    for message in client.chat_completion(
+        messages=[{"role": "user", "content": formatted_prompt}],
+        max_tokens=500,
+        stream=True,
+    ):
+        response += message.choices[0].delta.content
+
+    # Append assistant message to the session state
+    st.session_state.messages.append({"role": "assistant", "content": response.strip()})
+    st.chat_message("assistant").write(response.strip())
+    st.button(f"🔊", key=f"voice_button_{len(st.session_state.messages)-2}", on_click=speak_text, args=(response.strip(), len(st.session_state.messages)-1))
+
+
+## https://wallpapercave.com/wp/wp9668133.jpg
+## https://wallpapercave.com/wp/wp14059461.jpg
+## https://wallpapercave.com/wp/wp8219187.jpg
+## https://wallpapercave.com/uwp/uwp4189566.png

requirements.txt

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+streamlit
+textblob
+python-dotenv
+langchain
+gtts
+playsound
+huggingface_hub