Merge branch 'main' of https://github.com/Deathshot78/ReviewSense

.gitattributes

@@ -1,4 +1,5 @@
 <<<<<<< HEAD
+<<<<<<< HEAD
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text

requirements.txt

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 langchain==0.3.27
 langchain-community==0.3.31
 gradio==5.49.1
@@ -13,4 +14,17 @@ datasets==4.0.0
 numpy==2.0.2
 accelerate==1.11.0
 aiohttp==3.13.1
-huggingface-hub==0.35.3
+huggingface-hub==0.35.3
+=======
+torch==2.8.0
+transformers==4.56.1
+pytorch-lightning==2.5.5
+torchmetrics==1.8.2
+sentencepiece==0.2.1
+pandas==2.2.2
+scikit-learn==1.6.1
+gradio==5.44.1
+matplotlib==3.10.0
+seaborn==0.13.2
+wordcloud==1.9.4
+>>>>>>> e6de3c4338f79386345fa6e4bba5b0666ad808da

scripts/app.py

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 # app.py
 
 import gradio as gr
@@ -277,4 +278,166 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 # --- Launch Command ---
 if __name__ == "__main__":
     chat_memory.clear() # Clear memory each time app starts
-    demo.launch(debug=True)
+    demo.launch(debug=True)
+=======
+import gradio as gr
+import os
+import torch
+import pandas as pd
+import re
+
+# --- IMPORTANT ---
+# This script assumes you have a 'models.py' file in the same directory
+# containing the definitions for all model and inference classes.
+try:
+    from models import (
+        ReviewSummarizer,
+        AspectAnalyzer,
+        AspectExtractor,
+        FineTunedSentimentClassifier
+    )
+except ImportError:
+    print("CRITICAL ERROR: Make sure 'models.py' exists and contains the required classes.")
+    # Define dummy classes if imports fail, so Gradio can at least launch with an error message.
+    class ReviewSummarizer: pass
+    class AspectAnalyzer: pass
+    class AspectExtractor: pass
+    class FineTunedSentimentClassifier: pass
+
+# --- Configuration ---
+# --- IMPORTANT: UPDATE THIS PATH ---
+# You need to provide the path to the best checkpoint file that was saved
+# during the training of your sentiment model.
+SENTIMENT_CHECKPOINT_PATH = "checkpoints/sentiment-binary-best-checkpoint.ckpt" # <-- CHANGE THIS
+
+# --- Pre-defined Aspect Dictionaries for Different Product Categories ---
+ASPECT_DICTIONARIES = {
+    "Phone": ['camera', 'battery', 'battery life', 'screen', 'performance', 'price', 'design'],
+    "Coffee Maker": ['ease of use', 'design', 'noise level', 'coffee quality', 'brew time', 'cleaning'],
+    "Book": ['plot', 'characters', 'writing style', 'pacing', 'ending'],
+    "Default": ['quality', 'price', 'service', 'design', 'features'] # A fallback list
+}
+
+
+# --- 1. Load All Models (Global Objects) ---
+print("--- Initializing all models for the Gradio App ---")
+sentiment_classifier, summarizer, aspect_analyzer, aspect_extractor = None, None, None, None
+try:
+    summarizer = ReviewSummarizer(force_cpu=True)
+    aspect_analyzer = AspectAnalyzer(force_cpu=True)
+    aspect_extractor = AspectExtractor(force_cpu=True)
+
+    if not os.path.exists(SENTIMENT_CHECKPOINT_PATH):
+        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+        print("!!! WARNING: Sentiment checkpoint path not found or not set.         !!!")
+        print(f"!!! Please update the 'SENTIMENT_CHECKPOINT_PATH' variable in app.py")
+        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+    else:
+        sentiment_classifier = FineTunedSentimentClassifier(
+            checkpoint_path=SENTIMENT_CHECKPOINT_PATH, force_cpu=True
+        )
+    print("\n--- All models loaded successfully ---\n")
+except Exception as e:
+    print(f"An error occurred during model initialization: {e}")
+
+
+# --- 2. Define the Core Analysis Function ---
+def analyze_review(review_text, product_category):
+    if not review_text:
+        return {"ERROR": "Please enter a review."}, "", None
+
+    # --- a. Overall Sentiment Analysis ---
+    if sentiment_classifier:
+        sentiment_result = sentiment_classifier.classify(review_text)
+        sentiment_output = {
+            sentiment_result['label']: f"{sentiment_result['score']:.2f}"
+        }
+    else:
+        sentiment_output = {"ERROR": "Fine-tuned model not loaded. Check path."}
+
+    # --- b. Review Summarization ---
+    if summarizer:
+        summary_output = summarizer.summarize(review_text)
+    else:
+        summary_output = "ERROR: Summarizer model not loaded."
+
+    # --- c. Dynamic Aspect Extraction & Analysis ---
+    aspect_df = None
+    if aspect_extractor and aspect_analyzer:
+        aspect_dictionary = ASPECT_DICTIONARIES.get(product_category, ASPECT_DICTIONARIES["Default"])
+        extracted_aspects = aspect_extractor.extract(review_text, aspect_dictionary=aspect_dictionary)
+
+        if extracted_aspects:
+            aspect_results = aspect_analyzer.analyze(review_text, extracted_aspects)
+            aspect_df = pd.DataFrame([
+                {'Aspect': aspect, 'Sentiment': result['sentiment'], 'Score': f"{result['score']:.2f}"}
+                for aspect, result in aspect_results.items()
+            ])
+
+    return sentiment_output, summary_output, aspect_df
+
+
+# --- 3. Build the Gradio Interface ---
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🛍️ ReviewSense: Product Review Analysis Engine")
+    gr.Markdown(
+        "Enter a product review and select the product category. The tool will automatically "
+        "detect relevant features and provide an overall sentiment score, a summary, and a "
+        "breakdown of sentiment towards each feature."
+    )
+
+    with gr.Row():
+        with gr.Column(scale=2):
+            review_input = gr.Textbox(
+                lines=10,
+                label="Enter Product Review Here",
+                placeholder="e.g., The camera is amazing, but the battery life is terrible..."
+            )
+            category_input = gr.Dropdown(
+                choices=list(ASPECT_DICTIONARIES.keys()),
+                label="Select Product Category",
+                value="Phone"
+            )
+            analyze_button = gr.Button("Analyze Review", variant="primary")
+
+        with gr.Column(scale=1):
+            gr.Markdown("### Overall Sentiment")
+            sentiment_output = gr.Label()
+
+            gr.Markdown("### Generated Summary")
+            summary_output = gr.Textbox(lines=5, label="Summary", interactive=False)
+
+            gr.Markdown("### Detected Aspect Sentiments")
+            aspect_output = gr.DataFrame(headers=["Aspect", "Sentiment", "Score"], label="Aspects", interactive=False)
+
+    # Connect the button to the function
+    analyze_button.click(
+        fn=analyze_review,
+        inputs=[review_input, category_input],
+        outputs=[sentiment_output, summary_output, aspect_output]
+    )
+
+    gr.Examples(
+        examples=[
+            [
+                "The camera on this phone is incredible, the pictures are professional quality. However, the battery life is a total disaster, it barely lasts half a day with light use. The screen is bright and responsive, which I love.",
+                "Phone"
+            ],
+            [
+                "I am absolutely in love with this coffee maker! It's incredibly easy to use, brews a perfect cup every single time, and the design looks fantastic on my countertop. It's also surprisingly quiet.",
+                "Coffee Maker"
+            ],
+            [
+                "An amazing story with characters that felt so real. The plot had me hooked from the first page, though I felt the ending was a bit rushed.",
+                "Book"
+            ]
+        ],
+        inputs=[review_input, category_input]
+    )
+
+
+# --- 4. Launch the App ---
+if __name__ == "__main__":
+    print("Launching Gradio App...")
+    demo.launch()
+>>>>>>> e6de3c4338f79386345fa6e4bba5b0666ad808da

scripts/main.py

@@ -1,3 +1,4 @@
+<<<<<<< HEAD
 # main.py
 
 import torch
@@ -209,4 +210,115 @@ if __name__ == "__main__":
                 break
         print("\n--- Chat session ended. ---")
 
-    print("\n--- Local Execution Finished ---")
+    print("\n--- Local Execution Finished ---")
+=======
+import os
+import torch
+import pandas as pd
+
+try:
+    from data_prepare import ReviewDataset, ReviewDataModule
+    from models import SentimentClassifier, ReviewSummarizer, AspectAnalyzer, FineTunedSentimentClassifier, AspectExtractor
+except ImportError:
+    print("CRITICAL ERROR: Make sure 'review_summarizer.py', 'aspect_extractor.py', and 'sentiment_classifier_model.py' are in the same directory.")
+    exit()
+
+# --- Configuration ---
+# --- IMPORTANT: UPDATE THIS PATH ---
+# You need to provide the path to the best checkpoint file that was saved
+# during the training of your sentiment model.
+SENTIMENT_CHECKPOINT_PATH = "checkpoints/sentiment-binary-best-checkpoint.ckpt"
+
+# --- Pre-defined Aspect Dictionaries for Different Product Categories ---
+ASPECT_DICTIONARIES = {
+    "Phone": ['camera', 'battery', 'battery life', 'screen', 'performance', 'price', 'design'],
+    "Coffee Maker": ['ease of use', 'design', 'noise level', 'coffee quality', 'brew time', 'cleaning'],
+    "Book": ['plot', 'characters', 'writing style', 'pacing', 'ending'],
+    "Default": ['quality', 'price', 'service', 'design', 'features'] # A fallback list
+}
+
+def main():
+    """
+    Main function to run the command-line review analysis tool.
+    """
+    # --- 1. Load All Models ---
+    print("--- Initializing all models ---")
+    sentiment_classifier, summarizer, aspect_analyzer, aspect_extractor = None, None, None, None
+    try:
+        summarizer = ReviewSummarizer(force_cpu=True)
+        aspect_analyzer = AspectAnalyzer(force_cpu=True)
+        aspect_extractor = AspectExtractor(force_cpu=True)
+
+        if not os.path.exists(SENTIMENT_CHECKPOINT_PATH):
+            print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+            print("!!! WARNING: Sentiment checkpoint path not found or not set.         !!!")
+            print(f"!!! Please update the 'SENTIMENT_CHECKPOINT_PATH' variable in main.py")
+            print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+        else:
+            sentiment_classifier = FineTunedSentimentClassifier(
+                checkpoint_path=SENTIMENT_CHECKPOINT_PATH, force_cpu=True
+            )
+        print("\n--- All models loaded successfully ---\n")
+    except Exception as e:
+        print(f"An error occurred during model initialization: {e}")
+        return
+
+    # --- 2. Interactive Loop ---
+    while True:
+        print("\n==================================================")
+        print("          Product Review Analysis Tool          ")
+        print("==================================================")
+
+        # Get user input
+        review_text = input("Enter the product review text (or type 'quit' to exit):\n> ")
+        if review_text.lower() == 'quit':
+            break
+
+        print("\nAvailable Product Categories:")
+        for i, category in enumerate(ASPECT_DICTIONARIES.keys(), 1):
+            print(f"{i}. {category}")
+
+        category_choice = input(f"Select a product category (1-{len(ASPECT_DICTIONARIES)}):\n> ")
+        try:
+            category_idx = int(category_choice) - 1
+            product_category = list(ASPECT_DICTIONARIES.keys())[category_idx]
+        except (ValueError, IndexError):
+            print("Invalid choice. Using 'Default' category.")
+            product_category = "Default"
+
+        # --- 3. Run Analysis ---
+        print("\n--- Analyzing Review... ---")
+
+        # a. Overall Sentiment
+        sentiment_result = sentiment_classifier.classify(review_text)
+
+        # b. Summary
+        summary_result = summarizer.summarize(review_text)
+
+        # c. Aspect Extraction and Analysis
+        aspect_dictionary = ASPECT_DICTIONARIES.get(product_category)
+        extracted_aspects = aspect_extractor.extract(review_text, aspect_dictionary)
+        aspect_results = None
+        if extracted_aspects:
+            aspect_results = aspect_analyzer.analyze(review_text, extracted_aspects)
+
+        # --- 4. Display Results ---
+        print("\n-------------------- ANALYSIS RESULTS --------------------")
+        print(f"\n[ Overall Sentiment ]")
+        print(f"  - Sentiment: {sentiment_result['label']} (Score: {sentiment_result['score']:.2f})")
+
+        print(f"\n[ Generated Summary ]")
+        print(f"  - {summary_result}")
+
+        print(f"\n[ Detected Aspect Sentiments ]")
+        if aspect_results:
+            for aspect, result in aspect_results.items():
+                print(f"  - {aspect.title()}: {result['sentiment']} (Score: {result['score']:.2f})")
+        else:
+            print("  - No relevant aspects from the dictionary were detected in the review.")
+        print("----------------------------------------------------------")
+
+
+if __name__ == "__main__":
+    main()
+>>>>>>> e6de3c4338f79386345fa6e4bba5b0666ad808da