Upload app.py

app.py

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+import gradio as gr
+import requests
+from datasets import load_dataset
+from dotenv import load_dotenv
+import os
+
+load_dotenv()
+HUGGINGFACE_TOKEN = os.getenv('HUGGINGFACE_TOKEN')
+
+ds = load_dataset("fka/awesome-chatgpt-prompts")
+
+# Hugging Face API configuration
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
+HEADERS = {"Authorization": f"Bearer {HUGGINGFACE_TOKEN}"}
+
+# Function to interact with the Hugging Face model
+def query_huggingface_api(input_text):
+    payload = {"inputs": input_text}
+    try:
+        response = requests.post(API_URL, headers=HEADERS, json=payload)
+        response.raise_for_status()  # Raise error for HTTP errors
+        return response.json()[0]["generated_text"]
+    except requests.exceptions.RequestException as e:
+        return f"Error: {str(e)}"
+
+# Gradio interface
+def chatbot(input_text):
+    response = query_huggingface_api(input_text)
+    return response
+
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Textbox(lines=2, placeholder="Type your message here..."),
+    outputs=gr.Textbox(),
+    title="AI Chatbot",
+    description="Chat with the AI powered by Hugging Face Mistral-7B-Instruct-v0.3.",
+)
+
+if __name__ == "__main__":
+    iface.launch()