Update app.py

app.py

@@ -1,64 +1,59 @@
 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)",
-        ),
-    ],
+from transformers import pipeline
+import openai
+
+# Chatbot using GPT model
+chatbot = pipeline("text-generation", model="gpt2")
+
+def chatbot_response(query):
+    response = chatbot(query, max_length=50)
+    return response[0]['generated_text']
+
+# Image Generator using Stable Diffusion
+image_generator = pipeline("text-to-image", model="CompVis/stable-diffusion-v1-4")
+
+def generate_image(prompt):
+    return image_generator(prompt)[0]['image']
+
+# Code Generator (Simple example for Arduino)
+def generate_code(prompt):
+    if "arduino" in prompt.lower():
+        return '''
+        void setup() {
+          pinMode(LED_BUILTIN, OUTPUT);
+        }
+        
+        void loop() {
+          digitalWrite(LED_BUILTIN, HIGH);
+          delay(1000);
+          digitalWrite(LED_BUILTIN, LOW);
+          delay(1000);
+        }
+        '''
+    return "Sorry, I don't have a code for this request."
+
+# Research Bot using OpenAI
+def research_bot(query):
+    openai.api_key = "YOUR_OPENAI_API_KEY"
+    response = openai.Completion.create(
+        engine="text-davinci-003",
+        prompt=query,
+        max_tokens=100
+    )
+    return response.choices[0].text.strip()
+
+# Logo Generator (Using a simple placeholder)
+def generate_logo(prompt):
+    return f"Logo for '{prompt}' is being generated..."
+
+# Create Gradio Interface
+iface = gr.Interface(
+    fn=[chatbot_response, generate_image, generate_code, research_bot, generate_logo],
+    inputs=["text", "text", "text", "text", "text"],
+    outputs=["text", "image", "text", "text", "text"],
+    live=True,
+    title="Nithin AI - All-in-One Tool",
+    description="Your one-stop solution for AI-generated images, code, chat, research, and logos."
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+iface.launch(share=True)