Update app.py

app.py

@@ -14,26 +14,35 @@ if not ZEPHYR_API_TOKEN or not SD_API_TOKEN:
 ZEPHYR_API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
 SD_API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
 
- def query_zephyr(linkedin_text):
-    # Ensure that all lines inside this function are indented at the same level
-    messages = [
-        {
-            "role": "system",
-            "content": "Prepare a prompt for Stable Diffusion for the following LinkedIn post:",
-        },
-        {"role": "user", "content": linkedin_text},
+def query_zephyr(linkedin_text):
+    # Construct the prompt to include the LinkedIn post with an instruction for Zephyr
+    instruction = "Prepare a prompt for Stable Diffusion for the following LinkedIn post:"
+    conversation = [
+        {"role": "system", "content": instruction},
+        {"role": "user", "content": linkedin_text}
     ]
-    headers = {"Authorization": f"Bearer {ZEPHYR_API_TOKEN}"}
+    
+    headers = {
+        "Authorization": f"Bearer {ZEPHYR_API_TOKEN}",
+        "Content-Type": "application/json",
+    }
+    
+    # Format the payload according to the Hugging Face Inference API documentation
     payload = {
         "inputs": {
             "past_user_inputs": [],
             "generated_responses": [],
             "text": linkedin_text,
-            "conversation": messages
-        }
+            "conversation": conversation,
+        },
     }
+    
     response = requests.post(ZEPHYR_API_URL, headers=headers, json=payload)
-    return response.json()
+    if response.status_code == 200:
+        return response.json()
+    else:
+        raise Exception(f"Failed to query Zephyr model, status code: {response.status_code}")
+
 
 
 def generate_image_from_prompt(prompt, negative_prompt, guidance_scale, width, height, num_inference_steps):