Update app.py

app.py

@@ -14,10 +14,19 @@ 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):
-    prompt = f"prepare a prompt for Stable Diffusion for the following Linkedin post: {linkedin_text}"
+ query_zephyr(linkedin_text):
+    messages = [
+        {
+            "role": "system",
+            "content": "Prepare a prompt for Stable Diffusion for the following LinkedIn post:",
+        },
+        {"role": "user", "content": linkedin_text},
+    ]
     headers = {"Authorization": f"Bearer {ZEPHYR_API_TOKEN}"}
-    response = requests.post(ZEPHYR_API_URL, headers=headers, json={"inputs": prompt})
+    # 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)
     return response.json()
 
 def generate_image_from_prompt(prompt, negative_prompt, guidance_scale, width, height, num_inference_steps):