Update app.py

app.py

@@ -15,20 +15,21 @@ load_dotenv()
 # Constants
 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"
+# Switching to HF Inference API for stability
+IMAGE_GENERATION_API = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
 
-# Initialize Groq client with minimal parameters
+# Initialize Groq client
 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
 LLM_MODELS = {
     "Mixtral 8x7B (Groq)": "mixtral-8x7b-32768",
     "Mistral 7B (HF)": "mistralai/Mixtral-7B-Instruct-v0.1",
-    "LLaMA 13B (HF)": "meta-llama/Llama-13b-hf"  # Note: May require approval; replace if needed
+    "LLaMA 13B (HF)": "meta-llama/Llama-13b-hf"
 }
 
 # Utility Functions
@@ -45,7 +46,7 @@ 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") and groq_client:  # Groq model
+    if model.startswith("mixtral") and groq_client:
         try:
             completion = groq_client.chat.completions.create(
                 messages=[{
@@ -61,8 +62,8 @@ def generate_tutor_output(subject, difficulty, student_input, model):
             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
+            return {"lesson": "Sorry, unable to generate lesson due to API issue.", "question": "N/A", "feedback": "Please try again or check your input."}
+    else:
         try:
             client = Client("https://api-inference.huggingface.co/models/" + model, hf_token=HF_TOKEN)
             response = client.predict(prompt, api_name="/generate")
@@ -71,18 +72,16 @@ def generate_tutor_output(subject, difficulty, student_input, model):
             st.warning(f"HF model {model} failed, falling back to Mixtral.")
             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"}
+            return {"lesson": "Sorry, unable to generate 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,
-            height=512,
-            api_name="/predict"
-        )
-        image = Image.open(result)
+        client = Client(IMAGE_GENERATION_API, hf_token=HF_TOKEN)
+        result = client.predict(prompt, api_name="/predict")
+        if isinstance(result, str):  # Handle file path or binary data
+            image = Image.open(result)
+        else:
+            image = Image.open(result)
         image.save(path)
         return path
     except Exception as e:
@@ -91,6 +90,9 @@ def generate_image(prompt, path='temp_image.png'):
 
 def generate_video(images, audio_text, language, speaker, path='temp_video.mp4'):
     try:
+        if not images or all(img is None for img in images):
+            st.error("No valid images to create video.")
+            return None
         audio_client = Client("habib926653/Multilingual-TTS")
         audio_result = audio_client.predict(
             text=audio_text,
@@ -106,8 +108,11 @@ def generate_video(images, audio_text, language, speaker, path='temp_video.mp4')
             f.write(audio_bytes)
         
         audio_clip = mp.AudioFileClip(audio_path)
-        duration_per_image = audio_clip.duration / len(images)
+        duration_per_image = audio_clip.duration / len([img for img in images if img])
         image_clips = [mp.ImageClip(img).set_duration(duration_per_image) for img in images if img]
+        if not image_clips:
+            st.error("No image clips generated.")
+            return None
         video = mp.concatenate_videoclips(image_clips, method="compose").set_audio(audio_clip)
         video.write_videofile(path, fps=24, codec='libx264')
         return path