Update app.py

app.py

@@ -1,64 +1,53 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
+import gradio
+from groq import Groq
 
+# Initialize the Groq client with your API key
+client = Groq(
+    api_key="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"
+)
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
+# Initialize message prompt
+def initialize_messages():
+    return [{
+        "role": "system",
+        "content": """You are an assistant that provides answers to FAQs regarding any flights or travel assistance."""
+    }]
+
+messages_prmt = initialize_messages()
+
+# Custom chatbot function
+def customLLMBot(user_input, history):
+    global messages_prmt
+
+    messages_prmt.append({"role": "user", "content": user_input})
+
+    response = client.chat.completions.create(
+        messages=messages_prmt,
+        model="llama3-8b-8192",
+    )
+
+    print(response)  # Optional: Debugging
+    LLM_reply = response.choices[0].message.content
+    messages_prmt.append({"role": "assistant", "content": LLM_reply})
+
+    return LLM_reply
+
+# Gradio interface
+iface = gradio.ChatInterface(
+    customLLMBot,
+    chatbot=gradio.Chatbot(height=500),
+    textbox=gradio.Textbox(
+        placeholder="Need help with flight bookings, visa info, travel insurance, or destination tips? Ask me anything! "),
+    title="FAQ ChatBot",
+    description="Chat bot for FAQ service in travel assistance",
+    theme="soft",
+    examples=[
+        "hi",
+        "When is the next flight to Bangalore from Cochin?",
+        "How much does it cost?"
     ],
+    submit_btn=True
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the interface
+iface.launch(share=True)