Upload try 01

app.py

@@ -1,64 +1,82 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
+from sentence_transformers import SentenceTransformer, util
 
-"""
-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")
+# Carregar modelos
+model_name = "deepset/roberta-base-squad2"
+qa_pipeline = pipeline('question-answering', model=model_name, tokenizer=model_name)
+chat_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+embed_model = SentenceTransformer('all-MiniLM-L6-v2')
 
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+class MultiModelQA:
+    def __init__(self, qa_pipeline, chat_client, embed_model):
+        self.qa_pipeline = qa_pipeline
+        self.chat_client = chat_client
+        self.embed_model = embed_model
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+    def answer_with_qa_model(self, question, context):
+        return self.qa_pipeline({'question': question, 'context': context})['answer']
 
-    messages.append({"role": "user", "content": message})
+    def answer_with_chat_model(self, question, system_message, max_tokens, temperature, top_p):
+        messages = [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": question}
+        ]
+        response = ""
+        for msg in self.chat_client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = msg.choices[0].delta.content
+            response += token
+        return response
 
-    response = ""
+    def comparar_semanticamente(self, resp1, resp2):
+        emb1 = self.embed_model.encode(resp1, convert_to_tensor=True)
+        emb2 = self.embed_model.encode(resp2, convert_to_tensor=True)
+        similarity = util.cos_sim(emb1, emb2).item()
+        return similarity
 
-    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
+multiqa = MultiModelQA(qa_pipeline, chat_client, embed_model)
 
 
+def responder_e_comparar(question, context, system_message, max_tokens, temperature, top_p):
+    qa_resp = multiqa.answer_with_qa_model(question, context)
+    chat_resp = multiqa.answer_with_chat_model(question, system_message, max_tokens, temperature, top_p)
+    similaridade = multiqa.comparar_semanticamente(qa_resp, chat_resp)
+
+    result = f"""### Resposta do modelo QA:
+{qa_resp}
+
+### Resposta do modelo Chat:
+{chat_resp}
+
+### Similaridade semântica (coseno): {similaridade:.2%}
 """
-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)",
-        ),
+    return result
+
+
+# Interface Gradio
+demo = gr.Interface(
+    fn=responder_e_comparar,
+    inputs=[
+        gr.Textbox(label="Pergunta"),
+        gr.Textbox(label="Contexto"),
+        gr.Textbox(value="Você é um assistente útil.", label="Mensagem do sistema"),
+        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Máximo de tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperatura"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"),
     ],
+    outputs=gr.Markdown(),
+    title="Comparador de Respostas de Modelos",
+    description="Compara as respostas de um modelo de QA e um modelo de chat (Zephyr-7B) e calcula a similaridade semântica entre elas."
 )
 
-
 if __name__ == "__main__":
     demo.launch()