Final Deployment: Lightweight UI engine

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Dockerfile

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+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

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+---
+title: Sentiment Analyzer
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: Streamlit template space
+---
+
+# Welcome to Streamlit!
+
+Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+
+If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
+forums](https://discuss.streamlit.io).

requirements.txt

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+streamlit
+transformers
+torch
+safetensors
+pandas
+plotly

src/streamlit_app.py

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+import streamlit as st
+from transformers import pipeline
+import os
+import pandas as pd
+from datetime import datetime
+import time
+import plotly.graph_objects as go
+
+# --- PAGE CONFIG ---
+st.set_page_config(page_title="Sentiment Analyzer AI | Bilingual Engine", page_icon="🌐", layout="wide")
+
+# --- PROFESSIONAL NEUMORPHIC / GLASS CSS ---
+st.markdown("""
+    <style>
+    .stApp {
+        background: linear-gradient(135deg, #12141d 0%, #1a1c2c 100%);
+        color: #ffffff;
+    }
+    div[data-baseweb="input"] {
+        background: rgba(255, 255, 255, 0.05) !important;
+        backdrop-filter: blur(10px) !important;
+        border-radius: 15px !important;
+        border: 1px solid rgba(255, 255, 255, 0.1) !important;
+        padding: 5px !important;
+    }
+    .glass-card {
+        background: rgba(255, 255, 255, 0.05);
+        backdrop-filter: blur(10px);
+        border-radius: 20px;
+        border: 1px solid rgba(255, 255, 255, 0.1);
+        padding: 30px;
+        margin-top: 20px;
+        margin-bottom: 25px;
+    }
+    .stButton>button {
+        background: linear-gradient(90deg, #4facfe 0%, #00f2fe 100%);
+        color: white;
+        border-radius: 12px;
+        font-weight: 600;
+        height: 3rem;
+    }
+    [data-testid="stMetricValue"] { color: #00f2fe; font-weight: 800; }
+    </style>
+    """, unsafe_allow_html=True)
+
+# --- DATA LOGGING ---
+FEEDBACK_FILE = "sentiment_feedback.csv"
+
+def save_smart_data(text, ai_label, ai_score, corrected_label=None):
+    needs_review = "YES" if 0.33 <= ai_score <= 0.65 else "NO"
+    new_data = {
+        "Timestamp": [datetime.now().strftime("%Y-%m-%d %H:%M:%S")],
+        "Text": [text],
+        "AI_Label": [ai_label],
+        "Confidence": [round(ai_score, 4)],
+        "Needs_Review": [needs_review],
+        "Corrected_Label": [corrected_label if corrected_label else ai_label]
+    }
+    df = pd.DataFrame(new_data)
+    if not os.path.isfile(FEEDBACK_FILE):
+        df.to_csv(FEEDBACK_FILE, index=False)
+    else:
+        df.to_csv(FEEDBACK_FILE, mode='a', header=False, index=False)
+
+# --- MODEL ENGINE PATH FIX ---
+# Change this line:
+MODEL_PATH = "SumedhGajbhiye/Sentiment-Analyzer" 
+
+@st.cache_resource
+def load_engine(path):
+    # This will now download the model from your Model Repo automatically
+    return pipeline("sentiment-analysis", model=path, tokenizer=path, device=-1)
+
+# --- HEADER SECTION ---
+st.title("Sentiment Analyzer")
+st.caption("Advanced Bilingual Sentiment Analysis for English, Hindi & Hinglish")
+
+# --- SIDEBAR HUD ---
+with st.sidebar:
+    st.markdown("### 🛠️ ENGINE STATUS")
+    if os.path.exists(FEEDBACK_FILE):
+        df_log = pd.read_csv(FEEDBACK_FILE)
+        st.metric("Total Ingested", len(df_log))
+        st.metric("Anomalies Flagged", len(df_log[df_log['Needs_Review'] == 'YES']))
+        st.download_button("📤 Export Dataset", df_log.to_csv(index=False), "engine_feedback.csv", "text/csv")
+    else:
+        st.info("Waiting for input...")
+
+# --- ANALYSIS INTERFACE ---
+# Check if model file exists locally
+if not os.path.exists("model.safetensors"):
+    st.error("System Core (model.safetensors) not found in directory!")
+else:
+    classifier = load_engine(MODEL_PATH)
+    
+    user_input = st.text_input(
+        "QUERY INPUT:", 
+        placeholder="Enter sentence (English/Hindi/Hinglish)...", 
+        key="main_input"
+    )
+
+    if user_input:
+        with st.status("Neural Scan in Progress...", expanded=False) as status:
+            result = classifier(user_input)[0]
+            status.update(label="Analysis Complete", state="complete")
+
+        label = result['label']
+        score = result['score']
+        
+        # Adjust these keys to match your specific model's output labels (e.g., LABEL_0, LABEL_1)
+        emoji_map = {"POSITIVE": "🟢", "NEUTRAL": "🟡", "NEGATIVE": "🔴"}
+        color = "#00ff88" if "POSITIVE" in label.upper() else "#ff4b4b" if "NEGATIVE" in label.upper() else "#ffaa00"
+
+        st.markdown(f'''
+            <div class="glass-card">
+                <h4 style="color: #888; margin:0;">RESULT</h4>
+                <h1 style="color: {color}; margin:0; font-size: 3rem;">{label} {emoji_map.get(label.upper(), "")}</h1>
+                <p style="color: #aaa;">Confidence: {score:.1%}</p>
+            </div>
+            ''', unsafe_allow_html=True)
+
+        # Gauge Chart
+        fig = go.Figure(go.Indicator(
+            mode = "gauge+number",
+            value = score * 100,
+            gauge = {'axis': {'range': [None, 100]}, 'bar': {'color': color}}
+        ))
+        fig.update_layout(height=250, paper_bgcolor='rgba(0,0,0,0)', font={'color': "#fff"})
+        st.plotly_chart(fig, use_container_width=True)
+
+        # Human Verification
+        c1, c2 = st.columns(2)
+        with c1:
+            if st.button("CONFIRM ACCURACY"):
+                save_smart_data(user_input, label, score)
+                st.toast("Saved!")
+        with c2:
+            correction = st.selectbox("CORRECT LABEL:", ["Positive", "Neutral", "Negative"])
+            if st.button("SAVE CORRECTION"):
+                save_smart_data(user_input, label, score, corrected_label=correction)
+                st.toast("Correction logged!")
+
+# --- LOGS ---
+if os.path.exists(FEEDBACK_FILE):
+    with st.expander("📂 VIEW RECENT LOGS"):
+        st.dataframe(pd.read_csv(FEEDBACK_FILE).tail(5), use_container_width=True)