Update app.py

app.py

@@ -14,7 +14,8 @@ 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"
 
- query_zephyr(linkedin_text):
+ def query_zephyr(linkedin_text):
+    # Ensure that all lines inside this function are indented at the same level
     messages = [
         {
             "role": "system",
@@ -23,12 +24,18 @@ SD_API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-dif
         {"role": "user", "content": linkedin_text},
     ]
     headers = {"Authorization": f"Bearer {ZEPHYR_API_TOKEN}"}
-    # Since your original approach doesn't directly use the transformers library, you need to construct the payload manually.
-    # Adjust the payload to mimic the structure needed for chat interactions.
-    payload = json.dumps({"inputs": {"past_user_inputs": [], "generated_responses": [], "text": linkedin_text, "conversation": messages}})
-    response = requests.post(ZEPHYR_API_URL, headers=headers, data=payload)
+    payload = {
+        "inputs": {
+            "past_user_inputs": [],
+            "generated_responses": [],
+            "text": linkedin_text,
+            "conversation": messages
+        }
+    }
+    response = requests.post(ZEPHYR_API_URL, headers=headers, json=payload)
     return response.json()
 
+
 def generate_image_from_prompt(prompt, negative_prompt, guidance_scale, width, height, num_inference_steps):
     headers = {"Authorization": f"Bearer {SD_API_TOKEN}"}
     payload = {