chatbot changes

app.py

@@ -1,45 +1,24 @@
 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 function
 
-#### **1. Ensure Data is Inserted Before Display**
-insert_data_if_empty()
+# Streamlit UI
+st.title("Twitter Sentiment Analysis Chatbot")
 
-#### **2. MongoDB Connection**
-collection = get_mongo_client()
+# 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())
 
-#### **3. Streamlit App to Display Data**
-st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot")
+# Chatbot Input
+st.subheader("Chat with the Sentiment Analysis Bot")
+user_input = st.text_input("Enter a tweet to analyze:")
 
-# 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)")
+if st.button("Analyze"):
+    if user_input:
+        response = chatbot_response(user_input)
+        st.write("**Sentiment Analysis Result:**", response)
     else:
-        st.warning("⚠️ Please enter a question or text for sentiment analysis.")
+        st.warning("Please enter a tweet.")

chatbot.py

@@ -1,46 +1,20 @@
-import streamlit as st
-import google.generativeai as genai
-from transformers import pipeline
-import os
-
-# 🔑 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.")
-
-# Load Sentiment Analysis Model (RoBERTa)
-sentiment_pipeline = pipeline("sentiment-analysis", model="cardiffnlp/twitter-roberta-base-sentiment")
-
-# Function to analyze sentiment
-def analyze_sentiment(text):
-    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 a readable format
-    sentiment_mapping = {
-        "LABEL_0": "Negative",
-        "LABEL_1": "Neutral",
-        "LABEL_2": "Positive"
-    }
-    return sentiment_mapping.get(label, "Unknown"), score
-
-# 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
+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})"

db.py

@@ -1,30 +1,21 @@
-import pandas as pd
-import requests
-import io
-from pymongo import MongoClient
+import pymongo
+import streamlit as st
 
-#### **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"]
+# 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
 
-#### **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}")
+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