Update app.py

app.py

@@ -162,45 +162,12 @@ def plot_average_scores(avg_scores):
     return fig
 
 
-def predict_career_and_courses(user_responses):
-    """
-    Given user responses, predict which career the user should pursue and recommend courses.
-    This function is illustrative and assumes an API like `google-generativeai` exists.
-    """
-    # Placeholder for API configuration and call
-    # Replace "YOUR_API_KEY" with your actual API key
-    api_key = "AIzaSyDLOQFHEYBvpsqR2maRc61fN8A7ylZ-8f4"  # Ensure this is securely handled and not hard-coded in production
-    generation_config = {
-        "temperature": 0.9,
-        "top_p": 1,
-        "top_k": 1,
-        "max_output_tokens": 2048,
-    }
-    safety_settings = [
-        {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
-        {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
-        {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
-        {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"},
-    ]
-
-    # Assume `genai.GenerativeModel` is a valid API client for a generative AI model
-    model = genai.GenerativeModel(model_name="gemini-1.0-pro",
-                                  generation_config=generation_config,
-                                  safety_settings=safety_settings,
-                                  api_key=api_key)  # Hypothetical API configuration
-
-    prompt_parts = ["predict career to be pursued based on responses (Please make this efficient)"]
-    # Hypothetical API call
-    response = model.generate_content(prompt_parts)
-    
-    return response.text
 
 
 # Streamlit app main function
 def main():
     st.title("Q&A Analysis App")
     uploaded_file = st.file_uploader("Choose an Excel file with Q&A", type=["xlsx"])
-    user_responses = uploaded_file
 
     if uploaded_file is not None:
         df = pd.read_excel(uploaded_file)
@@ -214,13 +181,7 @@ def main():
         fig = plot_average_scores(avg_scores)
         st.pyplot(fig)
 
-    if st.button("Predict Career and Recommend Courses"):
-        if user_responses:
-            # Call the prediction function (placeholder)
-            prediction = predict_career_and_courses(user_responses)
-            st.write(prediction)
-        else:
-            st.write("Please enter your responses to proceed.")
+  
 
 
 def process_and_analyze_qa(df):