Delete frontend.py

frontend.py

@@ -1,225 +0,0 @@
-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)
-
-    api_token = st.text_input("🔐 API Token", type="password")
-    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 50–100 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": 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 ---
-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 and api_token:
-        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/default
-                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,
-                            }
-                            headers = {"x-api-key": api_token}
-                            params = {"smart": "1"} if use_smart_summary_bulk else {}
-                            res = requests.post(f"{backend_url}/bulk/", json=payload, headers=headers, params=params)
-
-                            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}")
-
-