Update app.py

app.py

@@ -4,16 +4,36 @@ from groq import Groq
 import os
 import json
 
-# Get Groq API key from environment variables
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-if not GROQ_API_KEY:
-    raise ValueError("Please set GROQ_API_KEY in the Space settings under 'Variables'.")
-
 # Initialize Groq client
-client = Groq(api_key=GROQ_API_KEY)
+try:
+    GROQ_API_KEY = os.environ["GROQ_API_KEY"]
+    print("API Key:", GROQ_API_KEY)  # Debug print
+    client = Groq(api_key=GROQ_API_KEY)
+except KeyError as e:
+    raise ValueError("GROQ_API_KEY not found in environment variables. Please set it in the Space settings under 'Variables'.")
+
+# Define valid models (only the two specified models)
+valid_models = [
+    "qwen-2.5-32b",
+    "qwen-2.5-coder-32b"
+]
 
 # Function to generate tutor output (lesson, question, feedback)
-def generate_tutor_output(subject, difficulty, student_input, selected_model):
+def generate_tutor_output(subject, difficulty, student_input, model):
+    if not subject or not difficulty or not student_input:
+        return json.dumps({
+            "lesson": "Error: Please fill in all fields (subject, difficulty, and input).",
+            "question": "No question available",
+            "feedback": "No feedback available"
+        })
+
+    if model not in valid_models:
+        return json.dumps({
+            "lesson": f"Error: Invalid model selected: {model}. Please choose a valid model.",
+            "question": "No question available",
+            "feedback": "No feedback available"
+        })
+
     prompt = f"""
     You are an expert tutor in {subject} at the {difficulty} level. 
     The student has provided the following input: "{student_input}"
@@ -25,27 +45,33 @@ def generate_tutor_output(subject, difficulty, student_input, selected_model):
     
     Format your response as a JSON object with keys: "lesson", "question", "feedback"
     """
-    
+
     try:
+        print(f"Calling Groq API with model: {model}, subject: {subject}, difficulty: {difficulty}, input: {student_input}")
         completion = client.chat.completions.create(
             messages=[
                 {
                     "role": "system",
-                    "content": f"You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way and giving math examples. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students."
+                    "content": f"You are the world's best AI tutor, renowned for your ability to explain complex concepts in an engaging, clear, and memorable way with examples suitable for {difficulty} level students. Your expertise in {subject} is unparalleled, and you're adept at tailoring your teaching to {difficulty} level students. Your goal is to not just impart knowledge, but to inspire a love for learning and critical thinking.",
                 },
                 {
                     "role": "user",
                     "content": prompt,
                 }
             ],
-            model=selected_model,
-            temperature=0.6,
-            top_p=0.95,
+            model=model,
             max_tokens=1000,
         )
-        return completion.choices[0].message.content
+        response = completion.choices[0].message.content
+        print(f"Groq API Response: {response}")  # Debug print
+        return response
     except Exception as e:
-        return f"Error: {str(e)}"
+        print(f"Groq API Error: {str(e)}")
+        return json.dumps({
+            "lesson": f"Error: Could not generate lesson. API error: {str(e)}",
+            "question": "No question available",
+            "feedback": "No feedback available due to API error"
+        })
 
 # Inline CSS to match the original styling
 custom_css = """
@@ -126,51 +152,47 @@ with gr.Blocks(
     title="AI Tutor with Text and Visuals",
     css=custom_css
 ) as demo:
-    gr.Markdown("# 🎓 Your AI Tutor with Visuals & Images")
+    gr.Markdown("# 🎓 Learn & Explore")
 
     # Section for generating Text-based output
     with gr.Row():
         with gr.Column(scale=2):
-            # Input fields for subject, difficulty, model, and student input
             subject = gr.Dropdown(
-                ["Math", "Science", "History", "Literature", "Code", "AI"],
-                label="Subject",
+                ["Math", "Science", "History", "Geography", "Economics"], 
+                label="Subject", 
                 info="Choose the subject of your lesson"
             )
-            difficulty = gr.Radio(
-                ["Beginner", "Intermediate", "Advanced"],
-                label="Difficulty Level",
-                info="Select your proficiency level"
+            difficulty = gr.Dropdown(
+                ["Beginner", "Intermediate", "Advanced"], 
+                label="Difficulty Level", 
+                info="Select your difficulty level"
             )
-            model_selector = gr.Radio(
-                ["qwen-qwq-32b", "qwen-2.5-coder-32b"],
-                label="Select Model",
-                value="qwen-qwq-32b",
-                info="Choose the model for text generation"
+            model_select = gr.Dropdown(
+                valid_models,
+                label="AI Model",
+                value="qwen-2.5-32b",
+                info="Select the AI model to use"
             )
             student_input = gr.Textbox(
-                placeholder="Type your query here...",
-                label="Your Input",
+                placeholder="Type your query here...", 
+                label="Your Input", 
                 info="Enter the topic you want to learn"
             )
-            submit_button_text = gr.Button("Generate Lesson & Question", variant="primary")
-
+            submit_button = gr.Button("Generate Lesson and Question", variant="primary")
+        
         with gr.Column(scale=3):
-            # Output fields for lesson, question, and feedback
             lesson_output = gr.Markdown(label="Lesson", elem_classes="markdown-output")
             question_output = gr.Markdown(label="Comprehension Question", elem_classes="markdown-output")
             feedback_output = gr.Markdown(label="Feedback", elem_classes="markdown-output")
 
-    # Section for Visual output using iframe
+    # Section for Visual output using iframe (unchanged)
     with gr.Row():
         with gr.Column(scale=2):
-            # Note: We're using an iframe, so we don't need a model selector for image generation
             gr.Markdown("## Image Generation")
             gr.Markdown("Use the embedded tool below to generate images.")
             submit_button_visual = gr.Button("Open Visual Tool", variant="primary")
 
         with gr.Column(scale=3):
-            # Output field for the iframe
             visual_output = gr.HTML(label="Image Generation Tool")
 
     gr.HTML("""
@@ -181,20 +203,21 @@ with gr.Blocks(
 
     gr.Markdown("""
     ### How to Use
-    1. **Text Section**: Select a subject, difficulty, and model, type your query, and click 'Generate Lesson & Question' to get your personalized lesson, comprehension question, and feedback.
+    1. **Text Section**: Select a subject, difficulty level, and model, type your query, and click 'Generate Lesson and Question' to get your personalized lesson, question, and feedback.
     2. **Visual Section**: Click 'Open Visual Tool' to load the image generation tool, then use it to generate images.
     3. Review the AI-generated content to enhance your learning experience!
 
-    *Example*: Try "Explain the quicksort algorithm" for text, and use the image tool to generate "a photo of an astronaut riding a horse on mars".
+    *Example*: Try "Explain the water cycle" for a Beginner Science lesson, and use the image tool to generate "a photo of a rainforest ecosystem".
     """)
 
-    def process_output_text(subject, difficulty, student_input, selected_model):
+    def process_output(output):
+        print(f"Raw API Output: {output}")  # Debug print
         try:
-            tutor_output = generate_tutor_output(subject, difficulty, student_input, selected_model)
-            parsed = json.loads(tutor_output)
+            parsed = json.loads(output)
             return parsed["lesson"], parsed["question"], parsed["feedback"]
         except Exception as e:
-            return f"Error: {str(e)}", "No question available", "No feedback available"
+            print(f"JSON Parsing Error: {str(e)}")
+            return "Error parsing output", "No question available", "No feedback available"
 
     def load_visual_tool():
         return """
@@ -202,9 +225,9 @@ with gr.Blocks(
         """
 
     # Generate Text-based Output
-    submit_button_text.click(
-        fn=process_output_text,
-        inputs=[subject, difficulty, student_input, model_selector],
+    submit_button.click(
+        fn=lambda s, d, i, m: process_output(generate_tutor_output(s, d, i, m)),
+        inputs=[subject, difficulty, student_input, model_select],
         outputs=[lesson_output, question_output, feedback_output]
     )
 
@@ -215,4 +238,5 @@ with gr.Blocks(
         outputs=[visual_output]
     )
 
-demo.launch()
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)