Update backend.py

backend.py

@@ -18,25 +18,22 @@ client = Groq()
 
 def get_spectrogram_base64(audio_path):
     """
-    Generates a Mel-Spectrogram and converts it to a Base64 image string 
-    so that Llama Vision can 'look' at it.
+    Generates a Mel-Spectrogram, aggressively compresses it, and converts it to Base64.
     """
     try:
         y, sr = librosa.load(audio_path, sr=None)
-        fig, ax = plt.subplots(figsize=(8, 4))
+        fig, ax = plt.subplots(figsize=(6, 3)) # Slightly smaller dimensions
         
-        # Generate the Spectrogram (Focusing on heart frequencies up to 2000Hz)
         S = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128, fmax=2000)
         S_dB = librosa.power_to_db(S, ref=np.max)
         librosa.display.specshow(S_dB, sr=sr, fmax=2000, ax=ax, cmap='magma')
         
-        # Save the plot to an in-memory buffer instead of a file
         buf = io.BytesIO()
-        plt.savefig(buf, format='png', bbox_inches='tight')
-        plt.close(fig) # Prevent memory leaks
+        # dpi=72 ensures the image file size is extremely small and well under Groq's 4MB limit
+        plt.savefig(buf, format='png', bbox_inches='tight', dpi=72)
+        plt.close(fig) 
         buf.seek(0)
         
-        # Convert to Base64
         base64_image = base64.b64encode(buf.read()).decode('utf-8')
         return base64_image
     except Exception as e:
@@ -48,14 +45,14 @@ def generate_medical_advice_from_vision(base64_img):
     Uses Llama 3.2 Vision (via Groq) to look at the Spectrogram and diagnose it.
     """
     if not base64_img:
-        return "Error: Could not process the audio into a visual spectrogram for the AI."
+        return "Error: Could not process the audio into a visual spectrogram."
 
     prompt = """
     You are an AI medical assistant specializing in cardiology. Look closely at this Mel-Spectrogram of a patient's Phonocardiogram (heart sound). 
     
     Based on the visual patterns in this spectrogram:
     1. Does this look Normal or Abnormal?
-    2. What specific cardiovascular disease might this indicate (e.g., Aortic Stenosis, Mitral Regurgitation, Normal)? Give your best medical estimation.
+    2. What specific cardiovascular disease might this indicate (e.g., Aortic Stenosis, Mitral Regurgitation, Normal)?
     3. Recommend general lifestyle or exercise advice based on your estimation.
     4. Mention potential medication types usually associated with this.
     
@@ -64,7 +61,7 @@ def generate_medical_advice_from_vision(base64_img):
 
     try:
         response = client.chat.completions.create(
-            model="llama-3.2-11b-vision-preview", # Groq's Llama Vision Model
+            model="llama-3.2-90b-vision-preview", # Upgraded to the larger, more stable vision model
             messages=[
                 {
                     "role": "user",
@@ -79,13 +76,15 @@ def generate_medical_advice_from_vision(base64_img):
                     ]
                 }
             ],
-            temperature=0.2, # Low temperature to keep the AI focused
+            temperature=0.2, 
             max_tokens=300
         )
         return response.choices[0].message.content
     except Exception as e:
-        print(f"Groq Vision API Error: {e}")
-        return "Failed to analyze the image. Please ensure your Groq API key is valid and the Vision model is currently online."
+        # Instead of a generic message, we now capture the exact error from Groq
+        actual_error = str(e)
+        print(f"Groq Vision API Error: {actual_error}")
+        return f"API Error: {actual_error}\n\n(If you see a 404 or Model Not Found error, Groq might have temporarily rotated their vision models.)"
 
 def text_to_speech(text):
     """