Update app.py

app.py

@@ -1,64 +1,55 @@
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+import torch
+
+# Agent 1: Intent Classifier
+intent_classifier = pipeline("text-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli")
+
+def detect_intent(text):
+    labels = {
+        "weather": "The user wants to know the weather.",
+        "faq": "The user is asking for help.",
+        "smalltalk": "The user is making casual conversation."
+    }
+    scores = {}
+    for label, hypothesis in labels.items():
+        result = intent_classifier({"premise": text, "hypothesis": hypothesis})[0]
+        scores[label] = result['score'] if result['label'] == 'ENTAILMENT' else 0
+    return max(scores, key=scores.get)
+
+# Agent 2: Domain Logic
+def handle_logic(intent):
+    if intent == "weather":
+        return "It’s currently sunny and 26°C."
+    elif intent == "faq":
+        return "Please click on 'Forgot Password' to reset it."
+    else:
+        return "Haha, that’s funny! Tell me more."
+
+# Agent 3: NLG with DialoGPT
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
+
+def generate_reply(text):
+    input_ids = tokenizer.encode(text + tokenizer.eos_token, return_tensors='pt')
+    output = model.generate(input_ids, max_length=100, pad_token_id=tokenizer.eos_token_id)
+    reply = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return reply
+
+# Combined chatbot
+def chatbot(user_input):
+    intent = detect_intent(user_input)
+    logic = handle_logic(intent)
+    reply = generate_reply(logic)
+    return reply
+
+# Gradio interface
+uiface = gr.Interface(fn=chatbot,
+                     inputs=gr.Textbox(lines=2, placeholder="Type your message..."),
+                     outputs="text",
+                     title="Three-Agent Hugging Face Chatbot",
+                     description="Intent detection + domain logic + natural generation")
+
+uiface.launch()
+
+