Upload 2 files

app.py

@@ -0,0 +1,90 @@
+import google.generativeai as genai
+import gradio as gr
+
+# Your API key (replace with a new one if this doesn't work)
+GOOGLE_API_KEY = "AIzaSyA6B_OIML84hzYB4cQfKFDaixLMyXXMRps"  
+
+# Configure API
+genai.configure(api_key=GOOGLE_API_KEY)
+
+# Use the free model
+model = genai.GenerativeModel('gemini-1.5-flash-latest')
+
+def generate_recommendation(problem_type, dataset_size, num_features, feature_type, priority, additional_info):
+    prompt = f"""
+    You are an expert machine learning engineer specializing in algorithm selection.
+    Recommend machine learning algorithms for a project with these characteristics:
+    
+    1. Problem Type: {problem_type}
+    2. Dataset Size: {dataset_size}
+    3. Number of Features: {num_features}
+    4. Feature Types: {feature_type}
+    5. Priority: {priority}
+    6. Additional Information: {additional_info}
+    
+    Provide:
+    1. Top 3 ranked algorithm recommendations (most suitable first)
+    2. For each algorithm:
+       - Brief justification
+       - Strengths for this use case
+       - Potential limitations
+    3. Final recommendation with detailed comparison
+    
+    Format exactly like this:
+    
+    === TOP RECOMMENDATIONS ===
+    1. [Algorithm 1]
+    - Why: [Justification]
+    - Pros: [Strengths]
+    - Cons: [Limitations]
+    
+    2. [Algorithm 2]
+    - Why: [Justification]
+    - Pros: [Strengths]
+    - Cons: [Limitations]
+    
+    3. [Algorithm 3]
+    - Why: [Justification]
+    - Pros: [Strengths]
+    - Cons: [Limitations]
+    
+    === FINAL CHOICE ===
+    Best Algorithm: [Algorithm Name]
+    - Why Best: [Detailed comparison]
+    - Why Others Are Less Suitable: [Explanation]
+    """
+    
+    try:
+        response = model.generate_content(prompt)
+        return response.text
+    except Exception as e:
+        return f"Error: {e}\n\nTip: The API key may need enabling at https://aistudio.google.com/"
+
+# Create Gradio interface
+with gr.Blocks(title="ML Algorithm Recommender", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # Machine Learning Algorithm Recommender
+    Enter your project characteristics to get personalized algorithm recommendations
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            problem_type = gr.Textbox(label="Problem Type*", placeholder="classification, regression, clustering...")
+            dataset_size = gr.Textbox(label="Dataset Size*", placeholder="small, medium, large or specific number")
+            num_features = gr.Textbox(label="Number of Features*", placeholder="few, many, or specific number")
+            feature_type = gr.Textbox(label="Feature Types*", placeholder="numerical, categorical, mixed")
+            priority = gr.Textbox(label="Priority*", placeholder="accuracy, speed, interpretability")
+            additional_info = gr.Textbox(label="Additional Details (optional)", placeholder="Any other important information")
+            submit_btn = gr.Button("Get Recommendations", variant="primary")
+        
+        with gr.Column():
+            output = gr.Textbox(label="Recommendation Results", lines=20, interactive=False)
+    
+    submit_btn.click(
+        fn=generate_recommendation,
+        inputs=[problem_type, dataset_size, num_features, feature_type, priority, additional_info],
+        outputs=output
+    )
+
+if __name__ == "__main__":
+    demo.launch()

requirements (1).txt

@@ -0,0 +1,2 @@
+google-generativeai>=0.5.0
+gradio==4.44.0