Update app.py

app.py

@@ -1,51 +1,39 @@
 import streamlit as st
-import requests
-from transformers import pipeline
+import os
+from groq import Groq
 
-# Initialize the language model (Hugging Face's pre-trained model for text generation)
-generator = pipeline('text-generation', model='gpt-2')  # You can choose any suitable language model
-
-# Title and Description of the App
-st.title("AI-powered Opportunity Finder for Youth")
-st.write("Find scholarships, internships, competitions, and more based on your skills and interests using AI!")
-
-# Collect user input
-interests = st.text_input("Enter your interests (e.g., AI, web development, etc.)")
-skills = st.text_input("Enter your skills (e.g., Python, Data Analysis, etc.)")
-location = st.text_input("Enter your location")
-
-# Button to trigger the AI recommendations
-if st.button("Find Opportunities"):
-    # Combine the input into a prompt for the language model
-    prompt = f"Find scholarships, internships, competitions, and online courses for a person interested in {interests}, skilled in {skills}, and located in {location}. Provide recommendations with details like title, link, and description."
-    
-    # Use the language model to generate recommendations
-    response = generator(prompt, max_length=150, num_return_sequences=1)
+# Function to get recommendations from Groq AI based on user input
+def get_opportunities(user_interests, user_skills, user_location):
+    # Initialize Groq client using API key from environment variable
+    client = Groq(api_key=os.environ.get("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"))
     
-    # Parse and display the generated text
-    recommendations = response[0]['generated_text']
+    # Call Groq's recommendation system (chat API or similar)
+    response = client.chat.completions.create(
+        messages=[{
+            "role": "user",
+            "content": f"Find me opportunities like scholarships, internships, online courses, and career advice based on my interests: {user_interests}, skills: {user_skills}, and location: {user_location}"
+        }],
+        model="llama-3.3-70b-versatile",  # You can adjust the model if needed
+    )
     
-    # Display the generated recommendations
-    st.write("Recommended Opportunities based on your input:")
-    st.write(recommendations)
+    return response.choices[0].message.content
 
-    # Example to further integrate Groq API (if you need more personalized results)
-    # You can replace the following code with an actual API request to Groq for more detailed results
-    # Make sure to replace the placeholder with your Groq API endpoint and parameters
-    try:
-        api_response = requests.post("https://api.groq.com/recommendations", json={
-            "interests": interests,
-            "skills": skills,
-            "location": location
-        })
+# Streamlit App Interface
+st.title("AI-Powered Opportunity Finder for Youth")
 
-        if api_response.status_code == 200:
-            api_recommendations = api_response.json()
-            st.write("Groq API Recommendations:")
-            for rec in api_recommendations:
-                st.write(f"- {rec['title']}: {rec['link']}")
-        else:
-            st.write("Unable to fetch recommendations from Groq API.")
+# Collect user input for interests, skills, and location
+interests = st.text_input("Enter your interests (e.g., AI, Robotics, Software Engineering):")
+skills = st.text_input("Enter your skills (e.g., Python, Data Science, Web Development):")
+location = st.text_input("Enter your location (e.g., Gujrat, Pakistan):")
 
-    except Exception as e:
-        st.write(f"Error while fetching recommendations from Groq API: {e}")
+# Button to fetch opportunities
+if st.button("Find Opportunities"):
+    if interests and skills and location:
+        # Fetch recommendations using the Groq API
+        opportunities = get_opportunities(interests, skills, location)
+        
+        # Display the opportunities
+        st.subheader("Recommended Opportunities")
+        st.write(opportunities)
+    else:
+        st.error("Please fill all fields.")