Upload frontend.py

frontend.py

@@ -0,0 +1,225 @@
+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}")
+
+