Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import json
+import joblib
+import gdown
+import os
+
+# Function to download and load model from Google Drive
+def load_model_from_drive(file_url, model_name):
+    """Downloads the model from Google Drive and loads it."""
+    # Specify where to save the model
+    model_folder = 'models'
+    if not os.path.exists(model_folder):
+        os.makedirs(model_folder)
+
+    # Download the model using gdown
+    output_path = os.path.join(model_folder, model_name)
+    gdown.download(file_url, output_path, quiet=False)
+
+    # Load and return the model using joblib
+    model = joblib.load(output_path)
+    return model
+
+# URLs for the models on Google Drive (using file IDs)
+distilbert_model_url = 'https://drive.google.com/uc?export=download&id=1WfjeGSQ7j4id1VSeGU8s2VzMBzNtImFT'
+bert_topic_model_url = 'https://drive.google.com/uc?export=download&id=164n8QfrQF4RB2LlQzGe1BbaFmugbzBGR'
+recommendation_model_url = 'https://drive.google.com/uc?export=download&id=17wFjVd9zTfHG33Eg7378Z6a1reohIkfE'
+
+# Model file names
+distilbert_model_name = 'distilbert_model.joblib'
+bert_topic_model_name = 'bertopic_model.joblib'
+recommendation_model_name = 'recommendation_model.joblib'
+
+# Load all three models
+distilbert_model = load_model_from_drive(distilbert_model_url, distilbert_model_name)
+bert_topic_model = load_model_from_drive(bert_topic_model_url, bert_topic_model_name)
+recommendation_model = load_model_from_drive(recommendation_model_url, recommendation_model_name)
+
+# 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":
+        # If the file is CSV, read it and extract all text content (even if unlabelled)
+        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":
+        # If the file is JSON, try to parse and extract the feedback text
+        json_data = json.load(file)
+        feedback_text = []
+        # Adjust this depending on how the JSON is structured (e.g., each feedback is a list of feedback entries)
+        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":
+        # If the file is plain text, read it directly
+        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 is extracted, analyze it
+    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.")