myassign.py

# ---------- Sentiment Analyzer Pro (With CSV Export and Language Support) ----------
import streamlit as st
import pandas as pd
import time
from transformers import pipeline
from io import StringIO
import requests

# -------------------- Configurations --------------------
st.set_page_config(
    page_title="Sentiment Analyzer Pro",
    page_icon="🧠",
    layout="wide",
)

# -------------------- Helper Functions --------------------
@st.cache_resource
def load_pipeline(language='en'):
    if language == 'ur':
        return pipeline("sentiment-analysis", model="urduhack/bert-base-urdu-cased-sentiment")
    return pipeline("sentiment-analysis")

classifier = load_pipeline()

def analyze_sentiment(text, language):
    result = classifier(text)[0]
    return result["label"], result["score"]

def create_csv(data):
    df = pd.DataFrame(data)
    csv = df.to_csv(index=False)
    return csv

def save_csv(data, filename="sentiment_analysis.csv"):
    csv = create_csv(data)
    st.download_button(
        label="Download CSV",
        data=csv,
        file_name=filename,
        mime="text/csv",
    )

def submit_contact_form(name, email, message):
    # [Optional] You can connect this with Google Forms or Email APIs
    st.success(f"✅ Thank you {name}, your message has been received!")

def show_footer():
    st.markdown("---")
    st.markdown(
        "<p style='text-align: center; font-size: 14px;'>© 2025 Sentiment Analyzer Pro | Built with ❤️ using Hugging Face and Streamlit</p>",
        unsafe_allow_html=True,
    )

# -------------------- Sidebar --------------------
st.sidebar.title("📚 Navigation")
page = st.sidebar.radio("Go to", ["Home", "About", "Contact"])

st.sidebar.title("📚 Use Cases (on Home)")
use_case = st.sidebar.radio(
    "Choose a Use Case",
    (
        "General Text",
        "Social Media Post",
        "Customer Review",
        "Email/Message",
        "Product Description",
    )
)

st.sidebar.title("🌐 Language")
language = st.sidebar.selectbox(
    "Select Language",
    ("English", "Urdu"),
)

st.sidebar.markdown("---")
st.sidebar.info(
    "This app uses a **Transformers-based model** to predict the **sentiment** "
    "(Positive, Negative, Neutral) with a **confidence score**."
)
st.sidebar.write("Made with ❤️ by M-Abdullah")

# -------------------- Page Logic --------------------

if page == "Home":
    # ------------- HOME PAGE -------------
    st.markdown("<h1 style='text-align: center; color: #4CAF50;'>🧠 Sentiment Analyzer Pro</h1>", unsafe_allow_html=True)
    st.markdown("<p style='text-align: center;'>Analyze emotions in text, reviews, posts, and more!</p>", unsafe_allow_html=True)
    st.write("---")
    
    st.write(f"### ✍️ Enter your {use_case} below:")
    user_input = st.text_area("", placeholder=f"Write your {use_case.lower()} here...", height=200)

    if st.button("🔍 Analyze Sentiment"):
        if user_input.strip() == "":
            st.warning("⚠️ Please enter some text to analyze!")
        else:
            with st.spinner('Analyzing...'):
                label, score = analyze_sentiment(user_input, language)
                time.sleep(1)  # Smooth spinner effect

            # Emoji Reaction
            emoji = {
                "POSITIVE": "😊",
                "NEGATIVE": "😞",
                "NEUTRAL": "😐"
            }.get(label.upper(), "🤔")

            # Result Display
            st.success(f"**Sentiment:** {label} {emoji}")
            st.info(f"**Confidence Score:** {score:.2f}")

            # Chart Section
            st.write("### 📊 Sentiment Score")
            st.bar_chart({"Confidence Score": [score]})

            # Detailed Explanation
            st.write("### 📖 Interpretation")
            if label == "POSITIVE":
                st.write("✅ This text expresses positive emotions, happiness, satisfaction, or support.")
            elif label == "NEGATIVE":
                st.write("⚠️ This text expresses negative emotions, dissatisfaction, criticism, or sadness.")
            else:
                st.write("ℹ️ This text is neutral without strong emotions.")
            
            # Download CSV Button
            save_csv([{"Text": user_input, "Sentiment": label, "Confidence Score": score}], "sentiment_analysis.csv")

elif page == "About":
    # ------------- ABOUT PAGE -------------
    st.markdown("<h1 style='text-align: center; color: #4CAF50;'>📘 About Sentiment Analyzer Pro</h1>", unsafe_allow_html=True)
    st.write("---")
    st.markdown("""
    ### ✨ Overview
    **Sentiment Analyzer Pro** is an advanced natural language processing (NLP) app built using:
    - 🛠 **Streamlit** for the web app interface
    - 🧠 **Hugging Face Transformers** for deep learning models
    
    This application can detect and classify **sentiments** (Positive, Negative, Neutral) in:
    - General texts
    - Social media posts (e.g., Twitter, Facebook)
    - Customer feedback
    - Emails and messaging
    - Product descriptions

    ### 🎯 Objectives
    - Simplify sentiment analysis for all users
    - Provide fast, reliable, real-time emotional analysis
    - Useful for businesses, researchers, students, and individuals

    ### 🚀 Future Enhancements
    - Multilingual Sentiment Analysis (Urdu, Arabic, French)
    - Custom Model Uploads
    - Sentiment Trends over Time
    """)
    
elif page == "Contact":
    FORM_URL = "https://docs.google.com/forms/u/0/d/1A95xalIMN6jDN8DCAq3agQGGDaDXP7x1hed-DjHqxow/prefill"
    FIELD_IDS = {
        "name": "entry.796411702",     # replace with actual ID
        "email": "entry.796411702",    # replace with actual ID
        "message": "entry.939908203",  # Correct ID
    }

    # Page Title
    st.markdown("<h1 style='text-align: center; color: #4CAF50;'>📬 Contact Us</h1>", unsafe_allow_html=True)
    st.markdown("---")

    st.markdown("""
    ### 💬 We'd Love to Hear From You!
    Fill out the form below and your message will be saved to our system.
    """)

    with st.form(key='contact_form'):
        name = st.text_input("Your Name")
        email = st.text_input("Your Email")
        message = st.text_area("Your Message")

        submit_button = st.form_submit_button(label='Send Message')

        if submit_button:
            if name.strip() == "" or email.strip() == "" or message.strip() == "":
                st.warning("⚠️ Please fill out all fields before submitting.")
            else:
                # Debugging: Check form values
                st.write(f"Name: {name}, Email: {email}, Message: {message}")
                
                # Submit data to Google Form
                data = {
                    FIELD_IDS["name"]: name,
                    FIELD_IDS["email"]: email,
                    FIELD_IDS["message"]: message,
                }
                response = requests.post(FORM_URL, data=data)

                if response.status_code == 200:
                    st.success(f"✅ Thank you {name}! Your message has been sent successfully.")
                else:
                    st.error(f"❌ There was a problem sending your message. Error: {response.status_code}")

    # Footer
    st.markdown("---")
    st.markdown("<p style='text-align: center; font-size: 16px;'>Made with ❤️ by <strong>M-Abdullah</strong> | 2025</p>", unsafe_allow_html=True)

# -------------------- Footer --------------------
show_footer()