Fix: Resolve merge markers and ensure correct v2.0 state

requirements.txt

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 langchain==0.3.27
 langchain-community==0.3.31
 gradio==5.49.1
@@ -15,16 +14,3 @@ numpy==2.0.2
 accelerate==1.11.0
 aiohttp==3.13.1
 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

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 # app.py
 
 import gradio as gr
@@ -279,165 +278,3 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 if __name__ == "__main__":
     chat_memory.clear() # Clear memory each time app starts
     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

@@ -1,4 +1,3 @@
-<<<<<<< HEAD
 # main.py
 
 import torch
@@ -211,114 +210,3 @@ if __name__ == "__main__":
         print("\n--- Chat session ended. ---")
 
     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