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| import streamlit as st | |
| import requests | |
| import pandas as pd | |
| from gtts import gTTS | |
| import base64 | |
| from io import BytesIO | |
| import os | |
| import plotly.express as px | |
| st.set_page_config(page_title="NeuroPulse AI", page_icon="🧠", layout="wide") | |
| if os.path.exists("logo.png"): | |
| st.image("logo.png", width=180) | |
| # Session state setup | |
| defaults = { | |
| "review": "", | |
| "dark_mode": False, | |
| "intelligence_mode": True, | |
| "trigger_example_analysis": False, | |
| "last_response": None, | |
| "followup_answer": None | |
| } | |
| for k, v in defaults.items(): | |
| if k not in st.session_state: | |
| st.session_state[k] = v | |
| # 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 settings | |
| 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) | |
| api_token = st.text_input("🔐 API Token", value="my-secret-key", type="password") | |
| 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", [ | |
| "Auto-detect", | |
| "distilbert-base-uncased-finetuned-sst-2-english", | |
| "nlptown/bert-base-multilingual-uncased-sentiment" | |
| ]) | |
| industry = st.selectbox("🏭 Industry", ["Auto-detect", "Generic", "E-commerce", "Healthcare", "Education"]) | |
| product_category = st.selectbox("🧩 Product Category", ["Auto-detect", "General", "Mobile Devices", "Laptops"]) | |
| use_aspects = st.checkbox("🔬 Enable Aspect Analysis") | |
| use_explain_bulk = st.checkbox("🧠 Generate Explanations (Bulk)") | |
| verbosity = st.radio("🗣️ Response Style", ["Brief", "Detailed"]) | |
| voice_lang = st.selectbox("🔈 Voice Language", ["en", "fr", "es", "de", "hi", "zh"]) | |
| # TTS | |
| 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) | |
| st.session_state.review = review | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| analyze = st.button("🔍 Analyze") | |
| with col2: | |
| if st.button("🎲 Example"): | |
| st.session_state.review = ( | |
| "I love this phone! Super fast performance, great battery, and smooth UI. " | |
| "Camera is awesome too, though the price is a bit high. Overall, very happy." | |
| ) | |
| st.session_state.trigger_example_analysis = True | |
| st.rerun() | |
| with col3: | |
| if st.button("🧹 Clear"): | |
| for key in ["review", "last_response", "followup_answer"]: | |
| st.session_state[key] = "" | |
| st.rerun() | |
| if (analyze or st.session_state.trigger_example_analysis) and st.session_state.review: | |
| st.session_state.trigger_example_analysis = False | |
| st.session_state.followup_answer = None | |
| with st.spinner("Analyzing..."): | |
| try: | |
| model = None if sentiment_model == "Auto-detect" else sentiment_model | |
| payload = { | |
| "text": st.session_state.review, | |
| "model": model or "distilbert-base-uncased-finetuned-sst-2-english", | |
| "industry": industry, | |
| "product_category": product_category, | |
| "verbosity": verbosity, | |
| "aspects": use_aspects, | |
| "intelligence": st.session_state.intelligence_mode | |
| } | |
| headers = {"x-api-key": api_token} | |
| res = requests.post(f"{backend_url}/analyze/", json=payload, headers=headers) | |
| if res.status_code == 200: | |
| st.session_state.last_response = res.json() | |
| else: | |
| st.error(f"API error: {res.status_code} - {res.json().get('detail')}") | |
| except Exception as e: | |
| st.error(f"🚫 Exception: {e}") | |
| data = st.session_state.last_response | |
| if data: | |
| st.subheader("📌 Summary") | |
| st.info(data["summary"]) | |
| st.caption("🧠 Summary Model: facebook/bart-large-cnn | " + verbosity + " response") | |
| st.markdown(f"**Context:** `{data['industry']}` | `{data['product_category']}` | `Web`") | |
| st.metric("📊 Sentiment", data["sentiment"]["label"], delta=f"{data['sentiment']['score']:.2%}") | |
| st.info(f"💢 Emotion: {data['emotion']}") | |
| st.subheader("🔊 Audio") | |
| audio = speak(data["summary"], lang=voice_lang) | |
| st.download_button("⬇️ Download Summary Audio", audio.read(), "summary.mp3") | |
| st.markdown("### 🔁 Got questions?") | |
| sample_questions = ["What did the user like most?", "Any complaints mentioned?", "Is it positive overall?"] | |
| selected_q = st.selectbox("💡 Sample Questions", ["Type your own..."] + sample_questions) | |
| custom_q = selected_q if selected_q != "Type your own..." else st.text_input("🔍 Ask a follow-up") | |
| if custom_q: | |
| with st.spinner("Thinking..."): | |
| try: | |
| follow_payload = { | |
| "text": st.session_state.review, | |
| "question": custom_q, | |
| "verbosity": verbosity | |
| } | |
| headers = {"x-api-key": api_token} | |
| res = requests.post(f"{backend_url}/followup/", json=follow_payload, headers=headers) | |
| if res.status_code == 200: | |
| st.session_state.followup_answer = res.json().get("answer") | |
| else: | |
| st.error(f"❌ Follow-up failed: {res.json().get('detail')}") | |
| except Exception as e: | |
| st.error(f"⚠️ Follow-up error: {e}") | |
| if st.session_state.followup_answer: | |
| st.subheader("🔁 Follow-Up Answer") | |
| st.success(st.session_state.followup_answer) | |
| # ==== BULK CSV ==== | |
| with tab2: | |
| st.title("📚 Bulk CSV Upload") | |
| st.markdown(""" | |
| Upload a CSV with columns:<br> | |
| <code>review</code>, <code>industry</code>, <code>product_category</code>, <code>device</code>, <code>follow_up</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: | |
| for col in ["industry", "product_category", "device", "follow_up"]: | |
| if col not in df.columns: | |
| df[col] = ["Auto-detect"] * len(df) | |
| df[col] = df[col].fillna("Auto-detect").astype(str) | |
| 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": None if sentiment_model == "Auto-detect" else sentiment_model, | |
| "industry": df["industry"].tolist(), | |
| "product_category": df["product_category"].tolist(), | |
| "device": df["device"].tolist(), | |
| "follow_up": df["follow_up"].tolist(), | |
| "explain": use_explain_bulk, | |
| "aspects": use_aspects, | |
| "intelligence": st.session_state.intelligence_mode | |
| } | |
| 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.columns: | |
| 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')}") | |
| except Exception as e: | |
| st.error(f"🚨 Bulk Processing Error: {e}") | |
| except Exception as e: | |
| st.error(f"❌ File Read Error: {e}") | |