chatbot to changes

app.py

@@ -1,24 +1,45 @@
 import streamlit as st
-from chatbot import chatbot_response
-from db import get_mongo_data
 import pandas as pd
+from db import insert_data_if_empty, get_mongo_client
+from chatbot import chatbot_response  # Import chatbot functionality
 
-# Streamlit UI
-st.title("Twitter Sentiment Analysis Chatbot")
+#### **1. Ensure Data is Inserted Before Display**
+insert_data_if_empty()
 
-# Display MongoDB Data
-st.subheader("MongoDB Data (First 5 Rows)")
-mongo_data = get_mongo_data()
-if mongo_data is not None:
-    st.dataframe(pd.DataFrame(mongo_data).head())
+#### **2. MongoDB Connection**
+collection = get_mongo_client()
 
-# Chatbot Input
-st.subheader("Chat with the Sentiment Analysis Bot")
-user_input = st.text_input("Enter a tweet to analyze:")
+#### **3. Streamlit App UI**
+st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot")
 
-if st.button("Analyze"):
-    if user_input:
-        response = chatbot_response(user_input)
-        st.write("**Sentiment Analysis Result:**", response)
+# Show first 5 rows from MongoDB
+st.subheader("First 5 Rows from Database")
+data = list(collection.find({}, {"_id": 0}).limit(5))
+
+if data:
+    st.write(pd.DataFrame(data))
+else:
+    st.warning("⚠️ No data found. Try refreshing the app.")
+
+# Button to show full MongoDB data
+if st.button("Show Complete Data"):
+    all_data = list(collection.find({}, {"_id": 0}))
+    st.write(pd.DataFrame(all_data))
+
+#### **4. AI Chatbot with Sentiment Analysis**
+st.subheader("🤖 AI Chatbot with Sentiment Analysis")
+
+# User input for chatbot
+user_prompt = st.text_area("Ask AI something or paste text for sentiment analysis:")
+
+if st.button("Analyze Sentiment & Get AI Response"):
+    ai_response, sentiment_label, confidence = chatbot_response(user_prompt)
+
+    if ai_response:
+        st.write("### AI Response:")
+        st.write(ai_response)
+
+        st.write("### Sentiment Analysis:")
+        st.write(f"**Sentiment:** {sentiment_label} ({confidence:.2f} confidence)")
     else:
-        st.warning("Please enter a tweet.")
+        st.warning("⚠️ Please enter a question or text for sentiment analysis.")

chatbot.py

@@ -1,20 +1,56 @@
-from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
-
-# Load model and tokenizer
-model_path = "./model"  # Load from local directory to avoid connection issues
-model = AutoModelForSequenceClassification.from_pretrained(model_path)
-tokenizer = AutoTokenizer.from_pretrained(model_path)
-
-# Define sentiment analysis pipeline
-sentiment_analyzer = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
-
-def chatbot_response(text):
-    """Analyze sentiment using RoBERTa model."""
-    if not text.strip():
-        return "Invalid input. Please enter text."
-    
-    result = sentiment_analyzer(text)[0]
-    label = result["label"]
-    score = round(result["score"], 2)
-
-    return f"{label} (Confidence: {score})"
+import os
+import streamlit as st
+import google.generativeai as genai
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+
+# 🔑 Fetch API key from Hugging Face Secrets
+GEMINI_API_KEY = os.getenv("gemini_api")
+
+if GEMINI_API_KEY:
+    genai.configure(api_key=GEMINI_API_KEY)
+else:
+    st.error("⚠️ Google API key is missing! Set it in Hugging Face Secrets.")
+
+# Correct Model Path
+MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
+
+# Load Sentiment Analysis Model (Ensure the correct model is used)
+try:
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+    sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_NAME, tokenizer=tokenizer)
+except Exception as e:
+    st.error(f"❌ Error loading sentiment model: {e}")
+
+# Function to analyze sentiment
+def analyze_sentiment(text):
+    try:
+        sentiment_result = sentiment_pipeline(text)[0]
+        label = sentiment_result['label']  # Extract sentiment label (POSITIVE, NEGATIVE, NEUTRAL)
+        score = sentiment_result['score']  # Extract confidence score
+
+        # Convert labels to readable format
+        sentiment_mapping = {
+            "LABEL_0": "Negative",
+            "LABEL_1": "Neutral",
+            "LABEL_2": "Positive"
+        }
+        return sentiment_mapping.get(label, "Unknown"), score
+    except Exception as e:
+        return f"Error analyzing sentiment: {e}", None
+
+# Function to generate AI response & analyze sentiment
+def chatbot_response(user_prompt):
+    if not user_prompt:
+        return None, None, None
+
+    try:
+        # AI Response from Gemini
+        model = genai.GenerativeModel("gemini-1.5-pro")
+        ai_response = model.generate_content(user_prompt)
+
+        # Sentiment Analysis
+        sentiment_label, confidence = analyze_sentiment(user_prompt)
+
+        return ai_response.text, sentiment_label, confidence
+    except Exception as e:
+        return f"❌ Error: {e}", None, None

db.py

@@ -1,21 +1,30 @@
-import pymongo
-import streamlit as st
+import pandas as pd
+import requests
+import io
+from pymongo import MongoClient
 
-# MongoDB Connection
-def get_mongo_connection():
-    """Establish connection to MongoDB."""
-    try:
-        client = pymongo.MongoClient(st.secrets["mongo_uri"])
-        db = client["twitter_db"]
-        collection = db["sentiments"]
-        return collection
-    except Exception as e:
-        st.error(f"MongoDB connection error: {e}")
-        return None
+#### **1. MongoDB Connection**
+def get_mongo_client():
+    client = MongoClient("mongodb+srv://groupA:pythongroupA@sentimentcluster.4usfj.mongodb.net/?retryWrites=true&w=majority&appName=SentimentCluster")
+    db = client["sentiment_db"]
+    return db["tweets"]
 
-def get_mongo_data():
-    """Fetch first 5 rows from MongoDB collection."""
-    collection = get_mongo_connection()
-    if collection:
-        return list(collection.find({}, {"_id": 0}).limit(5))
-    return None
+#### **2. Insert Data If Collection is Empty**
+def insert_data_if_empty():
+    collection = get_mongo_client()
+
+    if collection.count_documents({}) == 0:
+        print("🟢 No data found. Inserting dataset...")
+
+        csv_url = "https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv"
+
+        try:
+            response = requests.get(csv_url)
+            response.raise_for_status()  # Ensure request was successful
+            df = pd.read_csv(io.StringIO(response.text), encoding="ISO-8859-1")
+
+            # Insert into MongoDB
+            collection.insert_many(df.to_dict("records"))
+            print("✅ Data Inserted into MongoDB!")
+        except Exception as e:
+            print(f"❌ Error loading dataset: {e}")