first commit

app.py

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+
+import gradio as gr
+from together import Together
+import os
+
+
+client = Together(api_key=os.environ.get('TOGETHER_API_KEY'))
+
+
+# Function to create the system prompt based on the selected detector
+def create_system_prompt():
+    system_prompt = """You are a expert GMAT profiler who specializes in understanding student profile. You use successful candidate profile as a baseline and compare it with current profile and provide recomendations on the good profile.
+          Analyze the users input and provide guidance on what candidate can do to achieve a target college admission. 
+          Provide a detailed response in 500 words. Keep it bulleted. Only provide recommendations if user's profile is not good enough.
+          Always Provide the Response in following format only
+          Format -
+          Analysis : 4-5 lines
+          Recommendation : 4-5 points
+          Colleges attenable with current profile:
+          Target College Review and Recommendation:
+          """
+   
+    return system_prompt
+
+# Function to get response from OpenAI API
+def analyze_chat( gmat_score, gpa, target_college, work_experience, leadership_roles, extracurriculars, personal_statement):
+    system_prompt = create_system_prompt()
+    chat_input = """
+    User's Profile Data. Assess the following aspects:
+    - GMAT Score: {}
+    - Undergraduate GPA: {}
+    - Target College: {}
+    - Work Experience: {} years
+    - Leadership Roles: {}
+    - Extracurricular Activities: {}
+    - Personal Statement: {}
+    Provide a comprehensive analysis and recommendations.
+    """.format(gmat_score, gpa, target_college, work_experience, leadership_roles, extracurriculars, personal_statement)
+    response = client.chat.completions.create(
+        model="meta-llama/Llama-3.2-3B-Instruct-Turbo",  # Change to the OpenAI model you prefer
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": chat_input}
+        ],)
+    return response.choices[0].message.content.strip()
+
+# Gradio interface
+def gradio_interface(gmat_score, gpa, target_college,  work_experience, leadership_roles, extracurriculars, personal_statement):
+    return analyze_chat( gmat_score, gpa, target_college,  work_experience, leadership_roles, extracurriculars, personal_statement)
+
+# Custom CSS for input restriction
+custom_css = """
+#input-textbox textarea {
+    maxlength: 210;
+    overflow: hidden;
+    resize: none;
+}
+"""
+# Creating the Gradio UI
+with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.orange, secondary_hue=gr.themes.colors.sky,font=[gr.themes.GoogleFont("Inconsolata"), "Arial", "sans-serif"])) as demo:
+    with gr.Row():
+        gr.Markdown("## AI GMAT Profiler - Gozo Sensei")
+    with gr.Row():
+        with gr.Column(scale=2, min_width=300):
+            gmat_score = gr.Number(label="GMAT Score")
+            personal_statement = gr.TextArea(label="Enter your profile details", lines=4,elem_id="input-textbox",
+            info="Please ensure that any Personal Identifiable Information (PII) is removed before submitting the chat.")
+           
+        with gr.Column(scale=2, min_width=300):
+            gpa = gr.Number(label="Undergraduate GPA")
+            work_experience = gr.Number(label="Work Experience (years)")
+            target_college =  gr.Textbox(label="Target College")
+        with gr.Column(scale=2, min_width=300):
+            extracurriculars = gr.TextArea(label="Extracurricular Activities",lines=3)
+            leadership_roles = gr.TextArea(label="Leadership Roles",lines=3)
+    with gr.Row():
+        gr.Markdown("## Response")
+    with gr.Row():
+        output = gr.TextArea(label="Analysis",info="Disclaimer: The information provided below is generated by AI based on text analytics with limited context. It must not be considered as absolute truth or final judgment.", interactive = False, max_lines=20)
+    with gr.Row():
+        btn = gr.Button("Analyze")
+        btn.click(fn=gradio_interface, inputs=[gmat_score, gpa, target_college, work_experience, leadership_roles, extracurriculars, personal_statement], outputs=output)
+    
+# Launch the app
+demo.launch()
+

requirements.txt

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+gradio
+together