Minor modification made

Website/app.py

@@ -11,7 +11,7 @@
 from pages.closest_supermarket import closest_supermarket
 from pages.sentiment_analysis import sentiment_analysis
 from pages.time_series import show_feature5 as time_series
-# from pages.food_recommender import food_recommender as food_recommender
+from pages.food_recommender import food_recommender as food_recommender
 
 # Set page configuration
 st.set_page_config(page_title="SpicyBytes", layout="wide", initial_sidebar_state="collapsed")
@@ -76,7 +76,7 @@ def main():
         'Customer Inventory': cust_purchase_expected_expiry,
         'Dynamic Pricing': dynamic_pricing_streamlit,
         'Closest Supermarket': closest_supermarket,
-        'Food Recommendation': sentiment_analysis,
+        'Food Recommendation': food_recommender,
         'Sentiment Analysis': sentiment_analysis,
         'Time Series Analysis': time_series
     }

Website/pages/food_recommender.py

@@ -83,7 +83,7 @@ def preprocess_data(input_path):
 def setup_langchain():
     model_id = "meta-llama/Meta-Llama-3-8B"
     # Create a pipeline for text generation
-    text_generator = pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
+    text_generator = pipeline("text-generation", model=model_id, token="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", model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
 
     def generate_recipe(ingredients):
         prompt = f"As a food recommender, create different delicious food from these ingredients: {ingredients}. Include the food name, detailed steps, and estimated time."

Website/pages/sentiment_analysis.py

@@ -71,11 +71,12 @@ def display_reviews(df):
         "review": "Review",
         "Sentiment": "Sentiment",
         "sentiment_label": "Sentiment Label",
-        "sentiment_score": "Sentiment Score"
+        "sentiment_score": "Sentiment Score",
+        "user_name":"User Name"
     }, inplace=True)
     # Exclude 'time' column from display
     columns_to_display = [col for col in df.columns if col != 'time']
-    st.write(df[columns_to_display])
+    st.dataframe(df[columns_to_display], use_container_width=True)
 
 
 def sentiment_analysis():
@@ -144,7 +145,10 @@ def sentiment_analysis():
     df = df[(df['rating'] >= min_rating) & (df['rating'] <= max_rating)]
     df = df[(df['date'] >= start_date) & (df['date'] <= end_date)]
 
-    df['business_name'] = df['business_name'].apply(lambda x: x.title())
+    if review_type == "Business Reviews":
+        df['business_name'] = df['business_name'].apply(lambda x: x.title())
+    else:
+        df['user_name'] = df['user_name'].apply(lambda x: x.title())
     df['sentiment_label'] = df['sentiment_label'].apply(lambda x: x.title())
     df['review'] = df['review'].apply(lambda x: x.capitalize())