Update app.py

app.py

@@ -1,16 +1,14 @@
-from flask import Flask, render_template, request, send_from_directory
+from flask import Flask, render_template, request, send_from_directory, jsonify
 from datetime import datetime
+import requests
 from langchain_community.llms import HuggingFaceHub
 from langchain.prompts import PromptTemplate
-import requests
 import json
 import nltk
 from textblob import TextBlob
 from nltk.tokenize import word_tokenize
 from nltk.stem import PorterStemmer
 from nltk.stem import WordNetLemmatizer
-import tensorflow as tf
-from tensorflow import keras
 import spacy
 from bs4 import BeautifulSoup
 
@@ -18,7 +16,7 @@ nltk.download('punkt')
 nltk.download('wordnet')
 
 def download_spacy_model():
-    import spacy  # Import spacy within the function scope
+    import spacy
     try:
         spacy.load("en_core_web_sm")
     except OSError:
@@ -31,57 +29,31 @@ nlp = spacy.load("en_core_web_sm")
 
 app = Flask(__name__)
 
-
-# Load the JSON data from the file
-with open('ai_chatbot_data.json', 'r') as file:
-    json_data = json.load(file)
-
-    
-
-
-template = "Message: {message}\n\nSentiment Analysis: {sentiment}\n\nConversation Now Between you and user: {history}\n\nDate and Time: {date_time}\n\nBitcoin Price: ${bitcoin_price}\n\nBitcoin history from 1-jan-2024 to today the tidy is date-open-high-low-close-adj close-volum: {database_tag}\n\nYour system: {json_data}.\n\nResponse:"
-prompt = PromptTemplate(template=template, input_variables=["message", "sentiment", "history", "date_time", "bitcoin_price", "database_tag", "json_data"])
+template = "Message: {message}\n\nSentiment Analysis: {sentiment}\n\nConversation History: {history}\n\nDate and Time: {date_time}\n\nBitcoin Price: ${bitcoin_price}\n\nBitcoin Data: {database_tag}\n\nResponse: {response}"
+prompt = PromptTemplate(template=template, input_variables=["message", "sentiment", "history", "date_time", "bitcoin_price", "database_tag", "response"])
 conversation_history = []
 
 MAX_HISTORY_LENGTH = 55
 
-url = "https://dooratre-info.hf.space/"
-
-response = requests.get(url)
-soup = BeautifulSoup(response.content, 'html.parser')
-
-div_content = soup.find('div', {'id': '45'})
-if div_content:
-    print(div_content)
-else:
-    print("No div with id=45 found on the page.")
-database_tag=div_content
-
 def update_conversation_history(message):
     if len(conversation_history) >= MAX_HISTORY_LENGTH:
         conversation_history.pop(0)
     conversation_history.append(message)
 
-
 def get_bitcoin_price():
-    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
     url = 'https://api.coindesk.com/v1/bpi/currentprice.json'
     response = requests.get(url)
-
+    
     if response.status_code == 200:
         data = response.json()
         bitcoin_price = data['bpi']['USD']['rate']
+        current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
         return bitcoin_price, current_time
     else:
-        return 'Error fetching data', current_time
-
-@app.route('/assets/<path:path>')
-def send_static(path):
-    return send_from_directory('assets', path)
+        return 'Error fetching data', None
 
 @app.route('/')
 def index():
-    global conversation_history
     return render_template('index.html', conversation=conversation_history)
 
 @app.route('/submit', methods=['POST'])
@@ -92,39 +64,29 @@ def submit():
     tokens = [token.text for token in doc]
 
     sentiment = TextBlob(user_input).sentiment
-    
-    # Add Spacy NLP processing here
-    
+
     ps = PorterStemmer()
     stemmed_tokens = [ps.stem(token) for token in tokens]
 
     lemmatizer = WordNetLemmatizer()
     lemmatized_tokens = [lemmatizer.lemmatize(token) for token in tokens]
 
-    sentiment = TextBlob(user_input).sentiment
-
     bitcoin_price, current_time = get_bitcoin_price()
 
     conversation_history.append("User: " + user_input)
-    
-    # NLTK processing for conversation history
-    history_tokens = word_tokenize("<br>".join(conversation_history))
+
+    history_tokens = word_tokenize(" ".join(conversation_history))
     history_stemmed_tokens = [ps.stem(token) for token in history_tokens]
     history_lemmatized_tokens = [lemmatizer.lemmatize(token) for token in history_tokens]
 
-    model_input = prompt.format(message=user_input, sentiment=sentiment, history="<br>".join(conversation_history), database_tag=div_content, date_time=current_time, bitcoin_price=bitcoin_price, json_data=json_data,history_tokens=history_tokens,history_stemmed_tokens=history_stemmed_tokens,history_lemmatized_tokens=history_lemmatized_tokens)
-
-    response = llm(model_input, context="<br>".join(conversation_history))
-
-    bot_response = response.split('Response:')[1].strip()
-    bot_response = bot_response.strip().replace('\n', '<br>')
+    model_input = prompt.format(message=user_input, sentiment=sentiment, history=" ".join(conversation_history), database_tag="Placeholder", date_time=current_time, bitcoin_price=bitcoin_price, response="")
 
-    # Update the conversation history with bot's response
-    update_conversation_history("You " + bot_response)
+    response = "Placeholder response"  # Update with actual response generation logic
 
-    conversation_html = '<br>'.join(conversation_history)
+    response_message = "Bot: " + response
+    update_conversation_history(response_message)
 
-    return bot_response
+    return jsonify({'response':response})
 
 @app.route('/clear_history')
 def clear_history():