snvs

.gitattributes

@@ -1,4 +1,3 @@
-*.py filter=lfs diff=lfs merge=lfs -text
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,14 +1,12 @@
 ---
-title: Career Advisor
-emoji: 🏃
+title: Career Counselor
+emoji: 🌖
 colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 5.12.0
+colorTo: green
+sdk: streamlit
+sdk_version: 1.41.1
 app_file: app.py
 pinned: false
-license: afl-3.0
-short_description: Career Advice Matching your Skills and Likes
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,3 +1,71 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:ae5b76d534a8a9f2b4da07f3327e24991ce875d20ee6921a3fb6192ad5dfeed6
-size 2798
+import os
+import streamlit as st
+from groq import Groq
+
+# Securely set Groq API key (replace with your method for storing keys securely)
+GROQ_API_KEY = "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"  # Replace with your Groq API key
+os.environ["GROQ_API_KEY"] = GROQ_API_KEY
+
+# Initialize Groq client
+client = Groq(api_key=os.environ.get("GROQ_API_KEY"))
+
+# Title and Introduction
+st.title("Career Counselor App")
+st.write("I’m here to guide you toward the perfect career based on your skills, interests, and experience.")
+
+# User Input Section
+st.header("Tell us about yourself")
+age = st.number_input("Age:", min_value=18, max_value=65, step=1)
+education = st.text_input("Educational Background:")
+skills = st.text_area("List your skills (e.g., Python, teamwork, CAD):")
+interests = st.text_area("What areas of interest do you have? (e.g., AI, design, civil engineering):")
+experience = st.text_area("Describe your experience (if any):")
+
+# Function to get career suggestions from Groq
+def suggest_careers_groq(skills, interests, experience):
+    try:
+        # Prepare the prompt for Groq's chat model
+        prompt = f"""
+        Based on the following details, suggest suitable career paths, job market trends, and necessary qualifications:
+        Skills: {skills}
+        Interests: {interests}
+        Experience: {experience}
+        """
+        
+        # Call the Groq API
+        chat_completion = client.chat.completions.create(
+            messages=[
+                {
+                    "role": "user",
+                    "content": prompt,
+                }
+            ],
+            model="llama-3.3-70b-versatile",  # Specify the model
+            stream=False,
+        )
+        
+        # Extract and return the response
+        response_content = chat_completion.choices[0].message.content
+        return response_content
+    
+    except Exception as e:
+        st.error(f"An error occurred while contacting Groq API: {e}")
+        return None
+
+# Display recommendations based on user input
+if st.button("Get Career Advice"):
+    if not skills or not interests:
+        st.error("Please provide your skills and interests to get career advice.")
+    else:
+        st.subheader("Career Recommendations")
+        response = suggest_careers_groq(skills, interests, experience)
+        if response:
+            st.write(response)
+        else:
+            st.write("No recommendations available. Please try again later.")
+# Footer
+st.markdown("---")
+st.markdown(
+    "<p style='text-align: center; font-size: 14px;'>Designed by: <b>Engr. Makhdoom Muhammad Naeem Hashmi</b></p>",
+    unsafe_allow_html=True
+)

app_old_IBM.py

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:4938ab7a5b8e1d4dda4590d7665d617e5e93e7c50b9fd908e8834838348ca0a6
-size 3682

requirements.txt

@@ -1,3 +1,8 @@
-openai>=1.0.0
-gradio>=3.39
-python-dotenv
+streamlit
+groq
+requests
+
+
+
+
+

requirements_old.txt

@@ -1,3 +0,0 @@
-ibm-watson-machine-learning
-gradio
-python-dotenv