Upload frontend.py

frontend.py

@@ -0,0 +1,230 @@
+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}")