Update app.py

app.py

@@ -1,64 +1,62 @@
 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
-
-
-"""
-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)",
-        ),
-    ],
-)
-
-
+import openai
+import json
+import os
+from googlesearch import search
+
+# Set your OpenAI API Key (Replace with your actual key or use environment variables)
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Store chat history for each user
+user_histories = {}
+
+def google_search(query, num_results=3):
+    """Fetch top Google search results for a query."""
+    try:
+        results = [url for url in search(query, num_results=num_results)]
+        return "\n".join(results)
+    except Exception as e:
+        return f"Error fetching search results: {e}"
+
+def chat_with_ai(user_id, user_input):
+    """Handles chatbot conversation with history storage."""
+    if user_id not in user_histories:
+        user_histories[user_id] = []
+
+    # Add user input to chat history
+    user_histories[user_id].append({"role": "user", "content": user_input})
+
+    # Check if Google search is needed
+    if "search" in user_input.lower():
+        search_results = google_search(user_input)
+        return f"Here are the top search results:\n{search_results}"
+
+    # Call OpenAI GPT model
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=user_histories[user_id],
+        api_key=OPENAI_API_KEY
+    )
+
+    bot_reply = response["choices"][0]["message"]["content"]
+    
+    # Save bot reply to history
+    user_histories[user_id].append({"role": "assistant", "content": bot_reply})
+
+    return bot_reply
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# 🤖 AI Chatbot with Google Search & Memory")
+    
+    user_id = gr.Textbox(label="User ID (Enter any unique ID)", placeholder="Example: user123")
+    user_input = gr.Textbox(label="Your Message", placeholder="Ask me anything...")
+    output = gr.Textbox(label="Chatbot Response")
+
+    send_button = gr.Button("Send")
+
+    send_button.click(chat_with_ai, inputs=[user_id, user_input], outputs=output)
+
+# Launch the chatbot
 if __name__ == "__main__":
     demo.launch()