Update app.py

app.py

@@ -14,25 +14,20 @@ 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):
     headers = {
         "Authorization": f"Bearer {ZEPHYR_API_TOKEN}",
+        "Content-Type": "application/json",
     }
 
-    # Assuming you're formatting a request that includes conversational context
-    # and possibly leveraging detailed parameters like temperature and max_new_tokens
-    # Adjust these parameters as needed based on your requirements
+    # Assuming Zephyr supports a similar conversational structure
+    chat = [
+        {"role": "system", "content": "Prepare a prompt for Stable Diffusion for the following LinkedIn post:"},
+        {"role": "user", "content": linkedin_text},
+        # You can add more turns here if necessary
+    ]
+
     payload = {
-        "inputs": linkedin_text,
-        "parameters": {
-            "temperature": 0.7,
-            "max_new_tokens": 50,  # Control how long the generated response should be
-            # Add other parameters as needed
-        },
-        "options": {
-            "use_cache": False,  # Set to False to not use the cache
-            "wait_for_model": True,  # Wait for the model to be ready if it's currently loading
-        }
+        "inputs": chat,
     }
 
     response = requests.post(ZEPHYR_API_URL, headers=headers, json=payload)
@@ -40,9 +35,16 @@ def query_zephyr(linkedin_text):
         return response.json()
     else:
         print(f"Failed to query Zephyr model, status code: {response.status_code}")
-        print(response.text)  # To get more insight into what went wrong
-        return Noneto query Zephyr model, status code: {response.status_code}")
+        print(response.text)  # Provides insight into what went wrong
+        return None
 
+# Example LinkedIn post text
+linkedin_text = "Example LinkedIn post content here. How should this be visualized?"
+zephyr_response = query_zephyr_system_user_format(linkedin_text)
+if zephyr_response:
+    print(zephyr_response)
+else:
+    print("Error querying the Zephyr model.")
 
 
 def generate_image_from_prompt(prompt, negative_prompt, guidance_scale, width, height, num_inference_steps):