Upload myassign.py

myassign.py

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+# ---------- 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()