change to integrate mongodb

app.py

@@ -6,11 +6,12 @@ from chatbot import chatbot_response
 # Insert historical data into MongoDB if not already present.
 insert_data_if_empty()
 
-# Connect to MongoDB (this may be used for additional visualizations if needed).
+# Connect to MongoDB (for additional visualizations if needed).
 collection = get_mongo_client()
 
-st.subheader("💬 Chatbot with Sentiment, Topic Analysis, and Dataset Insights")
-user_prompt = st.text_area("Ask me something:")
+st.subheader("💬 Chatbot with Sentiment, Topic Analysis, and Dataset Entry Insights")
+user_prompt = st.text_area(
+    "Ask me something (e.g., 'What is the sentiment and category for the first data entry in the dataset'):")
 
 if st.button("Get AI Response"):
     ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence = chatbot_response(user_prompt)

chatbot.py

@@ -2,7 +2,7 @@ import os
 import streamlit as st
 import google.generativeai as genai
 from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
-from db import get_dataset_summary  # Import the dataset summary function
+from db import get_dataset_summary, get_entry_by_index
 
 # Configure Gemini API key
 GEMINI_API_KEY = os.getenv("gemini_api")
@@ -32,7 +32,6 @@ TOPIC_LABELS = [
     "Health", "Science", "Education", "Finance", "Travel", "Food"
 ]
 
-# Function to analyze sentiment using the pre-trained model
 def analyze_sentiment(text):
     try:
         sentiment_result = sentiment_pipeline(text)[0]
@@ -47,7 +46,6 @@ def analyze_sentiment(text):
     except Exception as e:
         return f"Error analyzing sentiment: {e}", None
 
-# Function to extract topic using zero-shot classification
 def extract_topic(text):
     try:
         topic_result = topic_pipeline(text, TOPIC_LABELS)
@@ -57,35 +55,67 @@ def extract_topic(text):
     except Exception as e:
         return f"Error extracting topic: {e}", None
 
-# Helper to check if the user query is about the dataset
 def is_dataset_query(prompt):
     keywords = ["dataset", "data", "csv", "mongodb", "historical"]
     return any(keyword in prompt.lower() for keyword in keywords)
 
-# Function to generate AI response along with sentiment and topic analysis
+def extract_entry_index(prompt):
+    # Map ordinal words to indices (0-indexed)
+    ordinals = {
+        "first": 0,
+        "1st": 0,
+        "second": 1,
+        "2nd": 1,
+        "third": 2,
+        "3rd": 2,
+        "fourth": 3,
+        "4th": 3,
+        "fifth": 4,
+        "5th": 4,
+    }
+    for word, index in ordinals.items():
+        if word in prompt.lower():
+            return index
+    return None
+
 def chatbot_response(user_prompt):
     if not user_prompt:
         return None, None, None, None, None
 
-    try:
-        # If the query seems related to the dataset, fetch summary insights.
-        if is_dataset_query(user_prompt):
-            dataset_insights = get_dataset_summary()
+    # Check if the query is about a specific dataset entry.
+    entry_index = extract_entry_index(user_prompt)
+    if entry_index is not None:
+        entry_text = get_entry_by_index(entry_index)
+        if entry_text:
+            # Create a combined prompt for Gemini to generate detailed insights.
             combined_prompt = (
-                f"{user_prompt}\n\nDataset Insights:\n{dataset_insights}\n\n"
-                "Provide a detailed answer that incorporates these dataset insights."
+                f"Analyze the following dataset entry from MongoDB:\n\n{entry_text}\n\n"
+                "Provide detailed insights, including sentiment analysis and category extraction."
             )
+            model_gen = genai.GenerativeModel("gemini-1.5-pro")
+            ai_response = model_gen.generate_content(combined_prompt)
+            # Analyze the entry text.
+            sentiment_label, sentiment_confidence = analyze_sentiment(entry_text)
+            topic_label, topic_confidence = extract_topic(entry_text)
+            return ai_response.text, sentiment_label, sentiment_confidence, topic_label, topic_confidence
         else:
-            combined_prompt = user_prompt
+            return f"❌ No entry found for index {entry_index+1}.", None, None, None, None
 
-        # Generate AI response using Gemini with the (possibly augmented) prompt.
-        model_gen = genai.GenerativeModel("gemini-1.5-pro")
-        ai_response = model_gen.generate_content(combined_prompt)
+    # Otherwise, if the query is about the dataset in general.
+    if is_dataset_query(user_prompt):
+        dataset_insights = get_dataset_summary()
+        combined_prompt = (
+            f"{user_prompt}\n\nDataset Insights:\n{dataset_insights}\n\n"
+            "Provide a detailed answer that incorporates these dataset insights."
+        )
+    else:
+        combined_prompt = user_prompt
 
-        # Perform sentiment analysis and topic extraction on the original user prompt.
-        sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
-        topic_label, topic_confidence = extract_topic(user_prompt)
+    model_gen = genai.GenerativeModel("gemini-1.5-pro")
+    ai_response = model_gen.generate_content(combined_prompt)
 
-        return ai_response.text, sentiment_label, sentiment_confidence, topic_label, topic_confidence
-    except Exception as e:
-        return f"❌ Error: {e}", None, None, None, None
+    # Run sentiment analysis and topic extraction on the original user prompt.
+    sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
+    topic_label, topic_confidence = extract_topic(user_prompt)
+
+    return ai_response.text, sentiment_label, sentiment_confidence, topic_label, topic_confidence

db.py

@@ -3,13 +3,11 @@ import requests
 import io
 from pymongo import MongoClient
 
-# Function to connect to MongoDB.
 def get_mongo_client():
     client = MongoClient("mongodb+srv://groupA:[email protected]/?retryWrites=true&w=majority&appName=SentimentCluster")
     db = client["sentiment_db"]
     return db["tweets"]
 
-# Function to insert data if the collection is empty.
 def insert_data_if_empty():
     collection = get_mongo_client()
     if collection.count_documents({}) == 0:
@@ -24,15 +22,12 @@ def insert_data_if_empty():
         except Exception as e:
             print(f"❌ Error loading dataset: {e}")
 
-# Function to get dataset summary from MongoDB.
 def get_dataset_summary():
     collection = get_mongo_client()
-    # Aggregate counts for each sentiment target.
     pipeline = [
         {"$group": {"_id": "$target", "count": {"$sum": 1}}}
     ]
     results = list(collection.aggregate(pipeline))
-    # Map the sentiment target values to labels.
     mapping = {"0": "Negative", "2": "Neutral", "4": "Positive"}
     summary_parts = []
     total = 0
@@ -44,3 +39,12 @@ def get_dataset_summary():
         summary_parts.append(f"{label}: {count}")
     summary = f"Total tweets: {total}. " + ", ".join(summary_parts) + "."
     return summary
+
+def get_entry_by_index(index):
+    collection = get_mongo_client()
+    # Sort by _id (assumes insertion order), skip to the requested index, and get one document.
+    document = collection.find({}, {"_id": 0}).sort("_id", 1).skip(index).limit(1)
+    docs = list(document)
+    if docs:
+        return docs[0].get("text", None)
+    return None