Update app.py

app.py

@@ -1,107 +1,46 @@
 import streamlit as st
-import google.generativeai as genai
 import os
+from groq import Groq
 
-# Configure Gemini API
-api_key = os.getenv("GEMINI_API_KEY")
+# Get Groq API Key from environment variables
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 
-if not api_key:
-    st.error("API key is missing! Please set GEMINI_API_KEY in your environment variables.")
+if not GROQ_API_KEY:
+    st.error("API key is missing! Please set GROQ_API_KEY in your environment variables.")
 else:
-    genai.configure(api_key=api_key)
-
-    def gemini_generate(prompt):
-        """Generate content using the Gemini model."""
-        model = genai.GenerativeModel("gemini-pro-latest")  # Ensure correct model name
-        response = model.generate_content(prompt)
-        return response.text
-
-    class GeminiAgent:
-        """AI agent that generates responses using the Gemini model."""
-        def __init__(self, role, goal, backstory, verbose=True):
-            self.role = role
-            self.goal = goal
-            self.backstory = backstory
-            self.verbose = verbose
-
-        def generate(self, prompt):
-            """Generate a response using Gemini AI."""
-            full_prompt = (
-                f"Role: {self.role}\n"
-                f"Goal: {self.goal}\n"
-                f"Backstory: {self.backstory}\n\n"
-                f"{prompt}"
-            )
-            if self.verbose:
-                print("Agent Prompt:", full_prompt)
-            return gemini_generate(full_prompt)
-
-    def create_agents(language="English"):
-        """Create AI agents with specific roles."""
-        researcher = GeminiAgent(
-            role="Educational Researcher",
-            goal="Analyze challenges and provide solutions for underserved schools, colleges, and universities.",
-            backstory="Expert in education infrastructure and policy for underserved regions.",
-            verbose=True
-        )
-
-        educator = GeminiAgent(
-            role="Education Communicator",
-            goal=f"Explain challenges and solutions in simple terms for {language} speakers.",
-            backstory=f"Skilled at translating research into easy-to-understand insights for {language} learners.",
-            verbose=True
-        )
-
-        return researcher, educator
-
-    researcher_agent, educator_agent = create_agents(language="English")
-
-    RELEVANT_KEYWORDS = {"school", "college", "university", "education", "students", "infrastructure", "learning"}
-
-    def is_relevant_query(user_input):
-        """Check if the query is related to education in underserved regions."""
-        return any(keyword in user_input.lower() for keyword in RELEVANT_KEYWORDS)
-
-    def get_chatbot_response(user_input):
-        """Process the query using AI agents."""
-        if not is_relevant_query(user_input):
-            return "I'm here to discuss education challenges in underserved regions. Please ask a relevant question."
-
-        researcher_prompt = (
-            "Analyze the following query to identify challenges and provide actionable solutions for "
-            "schools, colleges, or universities in underserved regions. Use examples where possible.\n\n"
+    client = Groq(api_key=GROQ_API_KEY)
+
+    def get_groq_response(user_input):
+        """Generate a response using the Groq API with the LLaMA model and system prompt."""
+        system_prompt = (
+            "Analyze the following query to identify potential challenges and actionable solutions "
+            "for schools, colleges, or universities in underserved regions. Provide detailed insights, "
+            "including possible names of institutions as examples where applicable.\n\n"
             f"User Query: {user_input}"
         )
 
         try:
-            research_response = researcher_agent.generate(researcher_prompt)
-        except Exception as e:
-            return f"Error in researcher agent: {str(e)}"
-
-        educator_prompt = (
-            "Explain the research findings in a simple and easy-to-understand manner.\n\n"
-            f"Analysis: {research_response}"
-        )
-
-        try:
-            educator_response = educator_agent.generate(educator_prompt)
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_input}
+                ],
+                model="llama3-70b-8192"
+            )
+            return chat_completion.choices[0].message.content
         except Exception as e:
-            return f"Error in educator agent: {str(e)}"
-
-        combined_response = (
-            "**🔍 Research Findings:**\n"
-            f"{research_response}\n\n"
-            "**📖 Simplified Explanation:**\n"
-            f"{educator_response}"
-        )
-        return combined_response
+            return f"Error: {str(e)}"
 
     # Streamlit UI
-    st.title("🎓 EduConnect Chatbot")
-    st.write("Ask about schools, colleges, and universities in underserved regions.")
+    st.title("EduConnect Chatbot (Groq LLaMA-3 70B)")
+    st.write("Ask about challenges and solutions for schools, colleges, or universities in underserved regions.")
 
     user_input = st.text_input("Enter your question:")
-    
+
     if st.button("Submit"):
-        response = get_chatbot_response(user_input)
-        st.write(response)
+        if user_input.strip():
+            response = get_groq_response(user_input)
+            st.write("### Response:")
+            st.write(response)
+        else:
+            st.warning("Please enter a question.")