Update app.py

app.py

@@ -1,9 +1,13 @@
 import gradio as gr
-from transformers import pipeline
-pipe = pipeline("text-generation", model="shivanikerai/TinyLlama-1.1B-Chat-v1.0-seo-optimised-title-suggestion-v1.0")
+import requests
+# from transformers import pipeline
+# pipe = pipeline("text-generation", model="shivanikerai/TinyLlama-1.1B-Chat-v1.0-seo-optimised-title-suggestion-v1.0")
+API_URL = "https://api-inference.huggingface.co/models/shivanikerai/TinyLlama-1.1B-Chat-v1.0-seo-optimised-title-suggestion-v1.0"
+def query(payload, api_token):
+	response = requests.post(API_URL, headers={"Authorization": f"Bearer {api_token}"}, json=payload)
+	return response.json()
 
-
-def my_function(keywords, product_info):
+def my_function(api_token, keywords, product_info):
     B_SYS, E_SYS = "<<SYS>>", "<</SYS>>"
     B_INST, E_INST = "[INST]", "[/INST]"
     B_in, E_in = "[Product Details]", "[/Product Details]"
@@ -11,8 +15,11 @@ def my_function(keywords, product_info):
     prompt = f"""{B_INST} {B_SYS} You are a helpful, respectful and honest assistant for ecommerce product title creation. {E_SYS}
     Create a SEO optimized e-commerce product title for the keywords:{keywords.strip()}
     {B_in}{product_info}{E_in}\n{E_INST}\n\n{B_out}"""
-    predictions = pipe(prompt)
-    output=((predictions[0]['generated_text']).split(B_out)[-1]).strip()
+    # predictions = pipe(prompt)
+    # output=((predictions[0]['generated_text']).split(B_out)[-1]).strip()
+    output = query({
+	"inputs": prompt,
+    },api_token)
     return (output)
 
     # Process the inputs (e.g., concatenate strings, perform calculations)
@@ -21,9 +28,9 @@ def my_function(keywords, product_info):
 
 # Create the Gradio interface
 interface = gr.Interface(fn=my_function, 
-                          inputs=["text", "text"], 
+                          inputs=["text", "text", "text"], 
                           outputs="text", 
                           title="SEO Optimised Title Suggestion", 
                           description="Enter Keywords and Product Info:")
 
-interface.launch()
+interface.launch()