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app.py

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+
+SYSTEM_PROMPT = "As an LLM, your primary function is to write engaging and informative scripts for TikTok videos based on facts about a given topic. Keep it concise and entertaining, but also make sure your scripts are accurate and factually correct."
+TITLE = "TikTok FactFinder"
+EXAMPLE_INPUT = "5 little-known facts about"
+import gradio as gr
+import os
+import requests
+
+zephyr_7b_beta = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta/"
+
+HF_TOKEN = os.getenv("HF_TOKEN")
+HEADERS = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+def build_input_prompt(message, chatbot, system_prompt):
+    """
+    Constructs the input prompt string from the chatbot interactions and the current message.
+    """
+    input_prompt = "<|system|>\n" + system_prompt + "</s>\n<|user|>\n"
+    for interaction in chatbot:
+        input_prompt = input_prompt + str(interaction[0]) + "</s>\n<|assistant|>\n" + str(interaction[1]) + "\n</s>\n<|user|>\n"
+
+    input_prompt = input_prompt + str(message) + "</s>\n<|assistant|>"
+    return input_prompt
+
+
+def post_request_beta(payload):
+    """
+    Sends a POST request to the predefined Zephyr-7b-Beta URL and returns the JSON response.
+    """
+    response = requests.post(zephyr_7b_beta, headers=HEADERS, json=payload)
+    response.raise_for_status()  # Will raise an HTTPError if the HTTP request returned an unsuccessful status code
+    return response.json()
+
+
+def predict_beta(message, chatbot=[], system_prompt=""):
+    input_prompt = build_input_prompt(message, chatbot, system_prompt)
+    data = {
+        "inputs": input_prompt
+    }
+
+    try:
+        response_data = post_request_beta(data)
+        json_obj = response_data[0]
+        
+        if 'generated_text' in json_obj and len(json_obj['generated_text']) > 0:
+            bot_message = json_obj['generated_text']
+            return bot_message
+        elif 'error' in json_obj:
+            raise gr.Error(json_obj['error'] + ' Please refresh and try again with smaller input prompt')
+        else:
+            warning_msg = f"Unexpected response: {json_obj}"
+            raise gr.Error(warning_msg)
+    except requests.HTTPError as e:
+        error_msg = f"Request failed with status code {e.response.status_code}"
+        raise gr.Error(error_msg)
+    except json.JSONDecodeError as e:
+        error_msg = f"Failed to decode response as JSON: {str(e)}"
+        raise gr.Error(error_msg)
+
+def test_preview_chatbot(message, history):
+    response = predict_beta(message, history, SYSTEM_PROMPT)
+    text_start = response.rfind("<|assistant|>", ) + len("<|assistant|>")
+    response = response[text_start:]
+    return response
+
+
+welcome_preview_message = f"""
+Welcome to **{TITLE}**! Say something like: 
+
+"{EXAMPLE_INPUT}"
+"""
+
+chatbot_preview = gr.Chatbot(layout="panel", value=[(None, welcome_preview_message)])
+textbox_preview = gr.Textbox(scale=7, container=False, value=EXAMPLE_INPUT)
+
+demo = gr.ChatInterface(test_preview_chatbot, chatbot=chatbot_preview, textbox=textbox_preview)
+
+demo.launch()