Update app.py

app.py

@@ -1,43 +1,65 @@
 # app.py
 
 # -----------------------------
-# 1️⃣ Hugging Face cache fix
+# 1️⃣ Set Hugging Face cache
 # -----------------------------
-# app.py ke top me (Streamlit import se pehle)
 import os
-os.environ["TRANSFORMERS_CACHE"] = "/tmp/cache"   # Hugging Face cache
-os.environ["NLTK_DATA"] = "/tmp/nltk_data"       # NLTK writable cache
-
-import nltk
-nltk.download('stopwords', download_dir="/tmp/nltk_data", quiet=True)
-
+os.environ["TRANSFORMERS_CACHE"] = "/tmp/cache"
+os.environ["NLTK_DATA"] = "/tmp/nltk_data"
 
 # -----------------------------
-# 2️⃣ Streamlit import
+# 2️⃣ Streamlit & NLP imports
 # -----------------------------
 import streamlit as st
+from mca_comment_analyzer import MCACommentAnalyzer
 
 # -----------------------------
-# 3️⃣ Local module import
+# 3️⃣ Streamlit UI
 # -----------------------------
-from mca_comment_analyzer import MCACommentAnalyzer  # check class name in your file
+st.set_page_config(page_title="MCA Comment Analyzer Light", layout="wide")
+st.title("📊 MCA Comment Analyzer Light 📝")
+st.sidebar.header("Upload or Enter Comments")
 
-# -----------------------------
-# 4️⃣ Streamlit App
-# -----------------------------
-st.title("MCA Comment Analyzer Light 📝")
+# Sidebar input
+upload_file = st.sidebar.file_uploader("Upload CSV/Excel/TXT", type=["csv","xlsx","txt"])
+manual_input = st.sidebar.text_area("Or enter comments manually (one per line)")
 
-# Example usage
-st.write("Upload your comment file or input text below:")
+comments = []
+if upload_file:
+    import pandas as pd
+    try:
+        if upload_file.name.endswith(".csv"):
+            df_file = pd.read_csv(upload_file)
+            if 'comment' in df_file.columns:
+                comments = df_file['comment'].astype(str).tolist()
+            else:
+                comments = df_file.iloc[:,0].astype(str).tolist()
+        elif upload_file.name.endswith(".xlsx"):
+            df_file = pd.read_excel(upload_file)
+            if 'comment' in df_file.columns:
+                comments = df_file['comment'].astype(str).tolist()
+            else:
+                comments = df_file.iloc[:,0].astype(str).tolist()
+        else:
+            comments = upload_file.read().decode("utf-8").splitlines()
+    except Exception as e:
+        st.error(f"File format not supported or corrupted. {e}")
+elif manual_input.strip():
+    comments = manual_input.strip().split("\n")
 
-user_input = st.text_area("Enter comment text:")
+if st.sidebar.button("Analyze"):
+    if comments:
+        analyzer = MCACommentAnalyzer()
+        df, keyword_freq = analyzer.process_comments(comments)
 
-if st.button("Analyze"):
-    if user_input.strip() == "":
-        st.warning("Please enter some text!")
-    else:
-        analyzer = MCACommentAnalyzer()              # class ka sahi naam
-        result = analyzer.analyze_comment(user_input)    # method ka sahi naam
-        st.success("Analysis Result:")
-        st.write(result)
+        st.subheader("📌 Analysis Results")
+        st.dataframe(df, use_container_width=True)
+
+        st.subheader("📊 Sentiment Distribution")
+        st.bar_chart(df["Sentiment"].value_counts())
 
+        st.subheader("☁️ Word Cloud")
+        plt_obj = analyzer.generate_wordcloud(keyword_freq)
+        st.pyplot(plt_obj)
+    else:
+        st.warning("⚠️ Provide comments manually or upload a supported file.")