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| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| from gtts import gTTS | |
| import base64 | |
| from io import BytesIO | |
| from PIL import Image | |
| import os | |
| import plotly.express as px | |
| st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide") | |
| # Load logo | |
| logo_path = "logo.png" | |
| if os.path.exists(logo_path): | |
| st.image(logo_path, width=180) | |
| # Session State defaults | |
| if "review" not in st.session_state: | |
| st.session_state.review = "" | |
| if "dark_mode" not in st.session_state: | |
| st.session_state.dark_mode = False | |
| if "intelligence_mode" not in st.session_state: | |
| st.session_state.intelligence_mode = True | |
| # Apply Dark Mode Styling | |
| if st.session_state.dark_mode: | |
| st.markdown(""" | |
| <style> | |
| html, body, [class*="st-"] { | |
| background-color: #121212; | |
| color: #f5f5f5; | |
| } | |
| .stTextInput > div > div > input, | |
| .stTextArea > div > textarea, | |
| .stSelectbox div div, | |
| .stDownloadButton > button, | |
| .stButton > button { | |
| background-color: #1e1e1e; | |
| color: white; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Sidebar controls | |
| with st.sidebar: | |
| st.header("⚙️ Global Settings") | |
| st.session_state.dark_mode = st.toggle("🌙 Dark Mode", value=st.session_state.dark_mode) | |
| st.session_state.intelligence_mode = st.toggle("🧠 Intelligence Mode", value=st.session_state.intelligence_mode) | |
| DEFAULT_DEMO_TOKEN = "my-secret-key" # 💡 replace with secure demo token | |
| api_token = st.text_input("🔐 API Token", value=DEFAULT_DEMO_TOKEN, type="password") | |
| # 🟡 Insert this warning right after the token field | |
| if not api_token or api_token.strip() == "my-secret-key": | |
| st.warning("🧪 Running in demo mode — for full access, enter a valid API key.") | |
| backend_url = st.text_input("🌐 Backend URL", value="http://localhost:8000") | |
| sentiment_model = st.selectbox("📊 Sentiment Model", [ | |
| "distilbert-base-uncased-finetuned-sst-2-english", | |
| "nlptown/bert-base-multilingual-uncased-sentiment" | |
| ]) | |
| industry = st.selectbox("🏭 Industry", [ | |
| "Auto-detect", "Generic", "E-commerce", "Healthcare", "Education", "Travel", "Banking", "Insurance", | |
| "Gaming", "Food Delivery", "Real Estate", "Fitness", "Entertainment" | |
| ]) | |
| product_category = st.selectbox("🧩 Product Category", [ | |
| "Auto-detect", "General", "Mobile Devices", "Laptops", "Healthcare Devices", "Banking App", | |
| "Travel Service", "Educational Tool", "Insurance Portal", "Streaming App", "Wearables", | |
| "Home Appliances", "Food Apps" | |
| ]) | |
| use_aspects = st.checkbox("🔬 Enable Aspect Analysis") | |
| use_smart_summary = st.checkbox("🧠 Smart Summary (Single)") | |
| use_smart_summary_bulk = st.checkbox("🧠 Smart Summary for Bulk") | |
| verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"]) | |
| follow_up = st.text_input("🔁 Follow-up Question") | |
| voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"]) | |
| # Text-to-Speech | |
| def speak(text, lang='en'): | |
| tts = gTTS(text, lang=lang) | |
| mp3 = BytesIO() | |
| tts.write_to_fp(mp3) | |
| b64 = base64.b64encode(mp3.getvalue()).decode() | |
| st.markdown(f'<audio controls><source src="data:audio/mp3;base64,{b64}" type="audio/mp3"></audio>', unsafe_allow_html=True) | |
| mp3.seek(0) | |
| return mp3 | |
| tab1, tab2 = st.tabs(["🧠 Single Review", "📚 Bulk CSV"]) | |
| # --- SINGLE REVIEW --- | |
| with tab1: | |
| st.title("🧠 NeuroPulse AI – Multimodal Review Analyzer") | |
| st.markdown("<div style='font-size:16px;color:#888;'>Minimum 20–50 words recommended.</div>", unsafe_allow_html=True) | |
| review = st.text_area("📝 Enter Review", value=st.session_state.review, height=180) | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| analyze = st.button("🔍 Analyze", use_container_width=True, disabled=not api_token) | |
| with col2: | |
| if st.button("🎲 Example", use_container_width=True): | |
| st.session_state.review = "I love this phone! Super fast performance, great battery, and smooth UI." | |
| st.rerun() | |
| with col3: | |
| if st.button("🧹 Clear", use_container_width=True): | |
| st.session_state.review = "" | |
| st.rerun() | |
| if analyze and review: | |
| if len(review.split()) < 50: | |
| st.warning("⚠️ Please enter at least 50 words.") | |
| else: | |
| with st.spinner("Analyzing..."): | |
| try: | |
| payload = { | |
| "text": review, | |
| "model": sentiment_model, | |
| "industry": industry, | |
| "aspects": use_aspects, | |
| "follow_up": follow_up, | |
| "product_category": product_category, | |
| "verbosity": verbosity, | |
| "intelligence": st.session_state.intelligence_mode | |
| } | |
| headers = {"x-api-key": st.session_state.get("api_token", api_token)} | |
| params = {"smart": "1"} if use_smart_summary else {} | |
| res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers, params=params) | |
| if res.status_code == 200: | |
| data = res.json() | |
| st.success("✅ Analysis Complete") | |
| st.subheader("📌 Summary") | |
| st.info(data["summary"]) | |
| st.caption(f"🧠 Summary Type: {'Smart' if use_smart_summary else 'Standard'} | {verbosity} Response") | |
| st.markdown(f"**Context:** `{data['industry']}` | `{data['product_category']}` | `Web`") | |
| st.subheader("🔊 Audio") | |
| audio = speak(data["summary"], lang=voice_lang) | |
| st.download_button("⬇️ Download Summary Audio", audio.read(), "summary.mp3", mime="audio/mp3") | |
| st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}") | |
| st.info(f"💢 Emotion: {data['emotion']}") | |
| if data.get("aspects"): | |
| st.subheader("🔍 Aspects") | |
| for a in data["aspects"]: | |
| st.write(f"🔹 {a['aspect']}: {a['sentiment']} ({a['score']:.2%})") | |
| if data.get("follow_up"): | |
| st.subheader("🔁 Follow-Up Answer") | |
| st.warning(data["follow_up"]) | |
| if data.get("explanation"): | |
| st.subheader("🧮 Explain This") | |
| st.markdown(data["explanation"]) | |
| else: | |
| st.error(f"❌ API Error {res.status_code}: {res.json().get('detail', 'Unknown error')}") | |
| except Exception as e: | |
| st.error(f"🚫 Exception occurred: {e}") | |
| # --- BULK CSV --- | |
| with tab2: | |
| st.title("📚 Bulk CSV Upload") | |
| st.markdown(""" | |
| Upload a CSV with the following columns:<br> | |
| <code>review</code> (required), <code>industry</code>, <code>product_category</code>, <code>device</code> (optional) | |
| """, unsafe_allow_html=True) | |
| with st.expander("📄 Sample CSV"): | |
| with open("sample_reviews.csv", "rb") as f: | |
| st.download_button("⬇️ Download sample CSV", f, file_name="sample_reviews.csv") | |
| uploaded_file = st.file_uploader("📁 Upload your CSV", type="csv") | |
| if uploaded_file: | |
| if not api_token: | |
| st.error("🔐 Please enter your API token in the sidebar.") | |
| else: | |
| try: | |
| df = pd.read_csv(uploaded_file) | |
| if "review" not in df.columns: | |
| st.error("CSV must contain a `review` column.") | |
| else: | |
| st.success(f"✅ Loaded {len(df)} reviews") | |
| for col in ["industry", "product_category", "device"]: | |
| if col not in df.columns: | |
| df[col] = ["Auto-detect"] * len(df) | |
| df[col] = df[col].fillna("Auto-detect").astype(str) | |
| # Replace "Auto-detect" with fallback | |
| df["industry"] = df["industry"].apply(lambda x: "Generic" if x.lower() == "auto-detect" else x) | |
| df["product_category"] = df["product_category"].apply(lambda x: "General" if x.lower() == "auto-detect" else x) | |
| df["device"] = df["device"].apply(lambda x: "Web" if x.lower() == "auto-detect" else x) | |
| if st.button("📊 Analyze Bulk Reviews", use_container_width=True): | |
| with st.spinner("Processing..."): | |
| try: | |
| payload = { | |
| "reviews": df["review"].tolist(), | |
| "model": sentiment_model, | |
| "aspects": use_aspects, | |
| "industry": df["industry"].tolist(), | |
| "product_category": df["product_category"].tolist(), | |
| "device": df["device"].tolist(), | |
| "intelligence": st.session_state.intelligence_mode, | |
| } | |
| # ✅ Updated: Pass token as query param (NOT in headers) | |
| res = requests.post( | |
| f"{backend_url}/bulk/?token={api_token}", | |
| json=payload | |
| ) | |
| if res.status_code == 200: | |
| results = pd.DataFrame(res.json()["results"]) | |
| st.dataframe(results) | |
| if "sentiment" in results: | |
| fig = px.pie(results, names="sentiment", title="Sentiment Distribution") | |
| st.plotly_chart(fig) | |
| st.download_button("⬇️ Download Results CSV", results.to_csv(index=False), "results.csv", mime="text/csv") | |
| else: | |
| st.error(f"❌ Bulk Error {res.status_code}: {res.json().get('detail', 'Unknown error')}") | |
| except Exception as e: | |
| st.error(f"🚨 Processing Error: {e}") | |
| except Exception as e: | |
| st.error(f"❌ File Read Error: {e}") | |