Update app.py

app.py

@@ -1,64 +1,44 @@
-import gradio as gr
+from fastapi import FastAPI, Request
+from pydantic import BaseModel
 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
-
-
-"""
-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 FastAPI app
+app = FastAPI()
 
+# Initialize Hugging Face Inference Client
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-if __name__ == "__main__":
-    demo.launch()
+# Define expected input format
+class InputData(BaseModel):
+    message: str  # User message sent from the frontend
+
+# Define the API endpoint
+@app.post("/api")
+async def get_ai_response(data: InputData):
+    try:
+        # Extract the user message from the request body
+        user_message = data.message
+
+        # Prepare messages for the model
+        messages = [
+            {"role": "system", "content": "You are a friendly Chatbot."},
+            {"role": "user", "content": user_message}
+        ]
+
+        # Generate response using the Hugging Face Inference API
+        response = ""
+        for message in client.chat_completion(
+            messages,
+            max_tokens=512,
+            stream=True,
+            temperature=0.7,
+            top_p=0.95,
+        ):
+            token = message.choices[0].delta.content
+            response += token
+
+        # Return the AI response as JSON
+        return {"response": response.strip()}
+    except Exception as e:
+        # Handle errors gracefully
+        return {"error": str(e)}