Update app.py

app.py

@@ -1,57 +1,90 @@
-# app.py
-
 import streamlit as st
-from modules.data_loader import load_texts
-from modules.preprocessing import preprocess_texts
-from modules.sentiment_analysis import analyze_sentiment
-from modules.summarizer import summarize_texts
-from modules.visualization import generate_wordcloud, plot_sentiment_distribution
-from modules.utils import save_results_to_csv, display_dataframe
-from config.settings import DATA_PATH
-
-# Title
-st.title("📊 Sentiment Analysis of E-Consultation Comments")
-st.write("AI-powered tool for sentiment analysis, summarization, and visualization.")
-
-# File uploader
-uploaded_file = st.file_uploader("Upload a .txt file (one comment per line)", type=["txt"])
-
-if uploaded_file:
-    # Save uploaded file
-    with open(DATA_PATH, "wb") as f:
-        f.write(uploaded_file.read())
-    st.success("✅ File uploaded successfully!")
-
-    # Load data
-    texts = load_texts(DATA_PATH)
-    st.write(f"Loaded **{len(texts)}** comments.")
-
-    # Preprocess
-    clean_texts = preprocess_texts(texts)
-
-    # Sentiment Analysis
-    st.subheader("🔍 Sentiment Analysis")
-    sentiment_results = analyze_sentiment(clean_texts)
-    sentiment_chart = plot_sentiment_distribution(sentiment_results)
-    st.image(sentiment_chart, caption="Sentiment Distribution")
-
-    # Summarization
-    st.subheader("📝 Summarization")
-    summaries = summarize_texts(clean_texts)
-    st.write("Here are some summaries:")
-    for s in summaries[:5]:
-        st.write(f"- {s['summary']}")
-
-    # Word Cloud
-    st.subheader("☁ Word Cloud")
-    wc_image = generate_wordcloud(clean_texts)
-    st.image(wc_image, caption="Word Cloud of Comments")
-
-    # Results Table
-    st.subheader("📑 Results Table")
-    df = display_dataframe(sentiment_results, summaries)
-    st.dataframe(df)
-
-    # Save results
-    results_path = save_results_to_csv(sentiment_results, summaries)
-    st.download_button("📥 Download Results CSV", data=open(results_path, "rb"), file_name="results.csv")
+import pandas as pd
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+from transformers import pipeline
+
+# Load Hugging Face models
+sentiment_analyzer = pipeline("sentiment-analysis")
+summarizer = pipeline("summarization")
+
+st.set_page_config(page_title="Sentiment Analysis App", layout="wide")
+
+st.title("📊 Sentiment Analysis of e-Consultation Comments")
+st.write("Analyze single or multiple comments: sentiment, summary, and word cloud.")
+
+# --- Sidebar mode selection ---
+mode = st.sidebar.radio("Choose mode:", ["Single Comment", "Upload File"])
+
+# --- Mode 1: Single Comment Analysis ---
+if mode == "Single Comment":
+    user_input = st.text_area("Enter a comment:", height=150)
+
+    if st.button("Analyze Comment"):
+        if user_input.strip():
+            # Sentiment
+            sentiment = sentiment_analyzer(user_input)[0]
+            st.subheader("🔹 Sentiment Analysis")
+            st.write(f"**Label:** {sentiment['label']} | **Score:** {sentiment['score']:.2f}")
+
+            # Summarization (if long enough)
+            if len(user_input.split()) > 30:
+                summary = summarizer(user_input, max_length=50, min_length=20, do_sample=False)[0]['summary_text']
+                st.subheader("🔹 Summary")
+                st.write(summary)
+            else:
+                st.info("Not enough text for summarization (need > 30 words).")
+
+            # Word Cloud
+            st.subheader("🔹 Word Cloud")
+            wordcloud = WordCloud(width=800, height=400, background_color="white").generate(user_input)
+            fig, ax = plt.subplots(figsize=(10, 5))
+            ax.imshow(wordcloud, interpolation="bilinear")
+            ax.axis("off")
+            st.pyplot(fig)
+        else:
+            st.warning("⚠️ Please enter a comment before analyzing.")
+
+# --- Mode 2: Batch Analysis from File ---
+else:
+    st.info("Upload a CSV or Excel file containing a column named **comment**.")
+
+    uploaded_file = st.file_uploader("Upload file", type=["csv", "xlsx"])
+
+    if uploaded_file:
+        # Read file
+        if uploaded_file.name.endswith(".csv"):
+            df = pd.read_csv(uploaded_file)
+        else:
+            df = pd.read_excel(uploaded_file)
+
+        if "comment" not in df.columns:
+            st.error("❌ File must contain a column named 'comment'.")
+        else:
+            st.write("### Uploaded Data", df.head())
+
+            if st.button("Analyze All Comments"):
+                sentiments = []
+                all_text = " "
+
+                for text in df["comment"].dropna():
+                    result = sentiment_analyzer(str(text))[0]
+                    sentiments.append(result["label"])
+                    all_text += " " + str(text)
+
+                df["sentiment"] = sentiments
+
+                st.subheader("🔹 Sentiment Results")
+                st.write(df)
+
+                # Overall Word Cloud
+                st.subheader("🔹 Word Cloud (All Comments)")
+                wordcloud = WordCloud(width=800, height=400, background_color="white").generate(all_text)
+                fig, ax = plt.subplots(figsize=(10, 5))
+                ax.imshow(wordcloud, interpolation="bilinear")
+                ax.axis("off")
+                st.pyplot(fig)
+
+                # Sentiment Distribution
+                st.subheader("🔹 Sentiment Distribution")
+                st.bar_chart(df["sentiment"].value_counts())