Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from scripts.load_models import distilbert_model, bert_topic_model, recommendation_model
+import gdown
+# Streamlit app layout
+st.title("Intelligent Customer Feedback Analyzer")
+st.write("Analyze customer feedback for sentiment, topics, and get personalized recommendations.")
+
+# User input for customer feedback file
+uploaded_file = st.file_uploader("Upload a Feedback File (CSV, JSON, TXT)", type=["csv", "json", "txt"])
+
+# Function to extract feedback text from different file formats
+def extract_feedback(file):
+    if file.type == "text/csv":
+        df = pd.read_csv(file)
+        feedback_text = []
+        for column in df.columns:
+            feedback_text.extend(df[column].dropna().astype(str).tolist())  # Include all text in the CSV
+        return feedback_text
+    elif file.type == "application/json":
+        json_data = json.load(file)
+        feedback_text = []
+        if isinstance(json_data, list):
+            feedback_text = [item.get('feedback', '') for item in json_data if 'feedback' in item]
+        elif isinstance(json_data, dict):
+            feedback_text = list(json_data.values())  # Include all values if feedback key doesn't exist
+        return feedback_text
+    elif file.type == "text/plain":
+        return [file.getvalue().decode("utf-8")]
+    else:
+        return ["Unsupported file type"]
+
+# Display error or feedback extraction status
+if uploaded_file:
+    feedback_text_list = extract_feedback(uploaded_file)
+
+    if feedback_text_list:
+        for feedback_text in feedback_text_list:
+            if st.button(f'Analyze Feedback: "{feedback_text[:30]}..."'):
+                # Sentiment Analysis
+                sentiment = distilbert_model.predict([feedback_text])
+                sentiment_result = 'Positive' if sentiment == 1 else 'Negative'
+                st.write(f"Sentiment: {sentiment_result}")
+
+                # Topic Modeling
+                topics = bert_topic_model.predict([feedback_text])
+                st.write(f"Predicted Topic(s): {topics}")
+
+                # Recommendation System
+                recommendations = recommendation_model.predict([feedback_text])
+                st.write(f"Recommended Actions: {recommendations}")
+    else:
+        st.error("Unable to extract feedback from the file.")
+else:
+    st.info("Please upload a feedback file to analyze.")
+