Update app.py

app.py

@@ -1,81 +1,47 @@
-from flask import Flask, request, jsonify, render_template
-from dotenv import load_dotenv
-import os
-
-# Import chatbot logic
-from chatbot import Chatbot
-
-# Load environment variables from .env file
-load_dotenv()
-
-app = Flask(__name__)
-
-# Initialize the chatbot
-chatbot = Chatbot()
-
-@app.route('/')
-def home():
-    return render_template('index.html')
-
-@app.route('/ask', methods=['POST'])
-def ask():
-    # Get the user's message
-    user_message = request.form['message']
-
-    # Get the response from the chatbot
-    response = chatbot.get_response(user_message)
-
-    return jsonify({'response': response})
-
-if __name__ == '__main__':
-    app.run(debug=True)
-    from .math import solve_math_problem
-from .english import correct_grammar
-from .science import explain_science_concept
-from .python import solve_python_code
-
-class Chatbot:
-    def __init__(self):
-        pass
-
-    def get_response(self, user_input):
-        # Determine the subject and return the appropriate response
-        if 'math' in user_input.lower():
-            return solve_math_problem(user_input)
-        elif 'grammar' in user_input.lower() or 'english' in user_input.lower():
-            return correct_grammar(user_input)
-        elif 'science' in user_input.lower():
-            return explain_science_concept(user_input)
-        elif 'python' in user_input.lower() or 'code' in user_input.lower():
-            return solve_python_code(user_input)
-        else:
-            return "Sorry, I don't understand. Can you please clarify your question?"
-            from sympy import symbols, Eq, solve
-
-def solve_math_problem(user_input):
-    # For simplicity, assume the user asks a solvable equation like 'solve x + 5 = 10'
-    try:
-        # Create symbolic variable x
-        x = symbols('x')
-
-        # Parse and solve the equation
-        equation = Eq(eval(user_input), 0)  # Simplified eval, you can make it more complex
-        solution = solve(equation, x)
-        
-        return f"The solution to {user_input} is {solution}"
-    except Exception as e:
-        return "Sorry, I couldn't solve the math problem. Please check your input."
-        from textblob import TextBlob
-
-def correct_grammar(user_input):
-    blob = TextBlob(user_input)
-    corrected = blob.correct()
-    
-    return f"Corrected sentence: {corrected}"
-    def explain_science_concept(user_input):
-    # Placeholder logic, can be expanded to integrate an external knowledge base or API
-    return f"Here's an explanation about {user_input}. [This can be extended further with a real knowledge base]."
-
-
-
-
+pip install streamlit requests
+import streamlit as st
+import requests
+
+# Set up the Groq API endpoint and your API key
+GROQ_API_URL = "https://api.groq.com/v1/chat/completions"
+GROQ_API_KEY = "your_groq_api_key_here"  # Replace with your actual Groq API key
+
+# Streamlit app title
+st.title("Educational Chatbot")
+
+# Initialize session state for chat history
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display chat messages from history on app rerun
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Accept user input
+if prompt := st.chat_input("What is your question?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message in chat message container
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Send user message to Groq API
+    headers = {
+        "Authorization": f"Bearer {GROQ_API_KEY}",
+        "Content-Type": "application/json"
+    }
+    data = {
+        "model": "groq-3.5-turbo",  # Replace with the model you want to use
+        "messages": st.session_state.messages
+    }
+    response = requests.post(GROQ_API_URL, headers=headers, json=data)
+    if response.status_code == 200:
+        chatbot_response = response.json()["choices"][0]["message"]["content"]
+        # Add chatbot response to chat history
+        st.session_state.messages.append({"role": "assistant", "content": chatbot_response})
+        # Display chatbot response in chat message container
+        with st.chat_message("assistant"):
+            st.markdown(chatbot_response)
+    else:
+        st.error("Failed to get response from Groq API")