Update app.py

app.py

@@ -17,8 +17,12 @@ HF_TOKEN = os.getenv("HF_TOKEN")
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 IMAGE_GENERATION_SPACE_NAME = "stabilityai/stable-diffusion-3.5-large-turbo"
 
-# Initialize Groq client
-groq_client = Groq(api_key=GROQ_API_KEY)
+# Initialize Groq client with minimal parameters
+try:
+    groq_client = Groq(api_key=GROQ_API_KEY)
+except Exception as e:
+    st.error(f"Failed to initialize Groq client: {e}")
+    groq_client = None
 
 # LLM Models (free options)
 LLM_MODELS = {
@@ -41,19 +45,23 @@ def generate_tutor_output(subject, difficulty, student_input, model):
     Format your response as a JSON object with keys: "lesson", "question", "feedback"
     """
     
-    if model.startswith("mixtral"):  # Groq model
-        completion = groq_client.chat.completions.create(
-            messages=[{
-                "role": "system",
-                "content": f"You are the world's best AI tutor for {subject}, renowned for clear, engaging explanations."
-            }, {
-                "role": "user",
-                "content": prompt
-            }],
-            model=model,
-            max_tokens=1000
-        )
-        return json.loads(completion.choices[0].message.content)
+    if model.startswith("mixtral") and groq_client:  # Groq model
+        try:
+            completion = groq_client.chat.completions.create(
+                messages=[{
+                    "role": "system",
+                    "content": f"You are the world's best AI tutor for {subject}, renowned for clear, engaging explanations."
+                }, {
+                    "role": "user",
+                    "content": prompt
+                }],
+                model=model,
+                max_tokens=1000
+            )
+            return json.loads(completion.choices[0].message.content)
+        except Exception as e:
+            st.error(f"Groq error: {e}")
+            return {"lesson": "Error generating lesson", "question": "N/A", "feedback": "N/A"}
     else:  # Hugging Face models
         try:
             client = Client("https://api-inference.huggingface.co/models/" + model, hf_token=HF_TOKEN)
@@ -61,16 +69,18 @@ def generate_tutor_output(subject, difficulty, student_input, model):
             return json.loads(response)
         except:
             st.warning(f"HF model {model} failed, falling back to Mixtral.")
-            return generate_tutor_output(subject, difficulty, student_input, "mixtral-8x7b-32768")
+            if groq_client:
+                return generate_tutor_output(subject, difficulty, student_input, "mixtral-8x7b-32768")
+            return {"lesson": "Error generating lesson", "question": "N/A", "feedback": "N/A"}
 
 def generate_image(prompt, path='temp_image.png'):
     try:
         client = Client(IMAGE_GENERATION_SPACE_NAME, hf_token=HF_TOKEN)
         result = client.predict(
             prompt=prompt,
-            width=512,  # Reduced for speed
+            width=512,
             height=512,
-            api_name="/predict"  # Correct endpoint
+            api_name="/predict"
         )
         image = Image.open(result)
         image.save(path)