Update app.py

app.py

@@ -1,29 +1,34 @@
-import torch
-from diffusers import DiffusionPipeline
 import gradio as gr
+import openai 
+import os
 
-# Load the diffusion pipeline for CogVideoX model
-pipe = DiffusionPipeline.from_pretrained("THUDM/CogVideoX-5b-I2V", torch_dtype=torch.float16)
+openai.api_key = os.getenv("GROQ_API_KEY")
+openai.api_base = "https://api.groq.com/openai/v1"
 
-def generate_image(prompt):
-    image = pipe(prompt).images[0]
-    return image
+def get_groq_response(message):
+  try:
+    response = openai.ChatCompletion.create(
+        model="llama-3.1-70b-versatile",
+        messages=[
+            
+            {"role": "user","content":message},
+        ]
+    )
+    return response.choices[0].message['content']
+    except Exception as e:
+    return f"Error: {str(e)}"
 
-# Set up Gradio interface
-with gr.Blocks() as demo:
-    gr.Markdown("# Image Generation using CogVideoX Diffusers")
-    
-    # Input prompt
-    prompt_input = gr.Textbox(label="Prompt", value="Astronaut in a jungle, cold color palette, muted colors, detailed, 8k")
-    
-    # Output image
-    output_image = gr.Image(label="Generated Image")
-    
-    # Button to trigger image generation
-    generate_button = gr.Button("Generate Image")
-    
-    # Bind button to function
-    generate_button.click(fn=generate_image, inputs=prompt_input, outputs=output_image)
+def chatbot(user_input , history =[]):
+      bot_response = get_groq_response(user_input)
+      history.append((user_input, bot_response))
+      return history, history
 
-# Launch Gradio app
-demo.launch()
+chat_interface = gr.Interface(
+        fn=chatbot,
+        inputs=["text","state"],
+        outputs=["chatbot","state"],
+        live=False,
+        title="My Chatbot",
+        description = "Mom: We have ChatGPT at home,\n ChatGPT at Home:"
+    )
+chat_interface.launch()