Update app.py

app.py

@@ -1,24 +1,51 @@
+import requests
+from bs4 import BeautifulSoup
+from transformers import AutoModelForCausalLM, AutoTokenizer
 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]})
+import torch
+
+# Web scraping
+def scrape_website(url):
+    response = requests.get(url)
+    soup = BeautifulSoup(response.text, 'html.parser')
+    content = ' '.join([p.text for p in soup.find_all('p')])
+    return content
+
+# Store data
+stored_data = {}
+def store_data(url, content):
+    stored_data[url] = content
+
+# Conversational AI with a smaller model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
+
+# Move model to GPU if available
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model.to(device)
+
+def generate_response(input_text):
+    input_ids = tokenizer.encode(input_text + tokenizer.eos_token, return_tensors='pt').to(device)
+    response_ids = model.generate(input_ids, max_length=50, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return response
+
+def chatbot_response(user_input):
+    if user_input.startswith('http'):
+        url = user_input
+        if url in stored_data:
+            content = stored_data[url]
+        else:
+            content = scrape_website(url)
+            store_data(url, content)
+        return "I've fetched the data from the website. How can I help you with it?"
+    else:
+        response = generate_response(user_input)
+        return response
+
+# Interface
+def chat_interface(user_input):
+    return chatbot_response(user_input)
+
+iface = gr.Interface(fn=chat_interface, inputs="text", outputs="text")
+iface.launch()