Update app.py

app.py

@@ -1,67 +1,169 @@
+# import gradio as gr
+# from huggingface_hub import InferenceClient
+# import os
+
+# """
+# 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
+# """
+
+# token = os.getenv("HF_TOKEN")  
+# client = InferenceClient("BioMistral/BioMistral-7B", token=token)
+
+
+# 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 gradio as gr
-from huggingface_hub import InferenceClient
-import os
+from langdetect import detect
+from transformers import pipeline
+from qdrant_client import QdrantClient
+from qdrant_client.models import VectorParams, Distance
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from langchain.vectorstores import Qdrant
+from transformers import GenerationConfig, FastLanguageModel
+from langchain.embeddings import HuggingFaceEmbeddings
 
-"""
-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
-"""
+# Define model path
+model_name = "FreedomIntelligence/Apollo-7B"
 
-token = os.getenv("HF_TOKEN")  
-client = InferenceClient("BioMistral/BioMistral-7B", token=token)
+# Load model with Unsloth (4-bit QLoRA)
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name=model_name,
+    max_seq_length=2048,
+    dtype=torch.float16,
+    load_in_4bit=True
+)
 
+# Enable padding token if missing
+tokenizer.pad_token = tokenizer.eos_token
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Set up Qdrant vector store
+qdrant_client = QdrantClient(url="https://your-qdrant-instance.com")
+vector_size = 768
+embedding = HuggingFaceEmbeddings(model_name="Omartificial-Intelligence-Space/GATE-AraBert-v1")
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+qdrant_vectorstore = Qdrant(
+    client=qdrant_client,
+    collection_name="arabic_rag_collection",
+    embeddings=embedding
+)
 
-    messages.append({"role": "user", "content": message})
+# Generation config
+generation_config = GenerationConfig(
+    max_new_tokens=150,
+    temperature=0.2,
+    top_k=20,
+    do_sample=True,
+    top_p=0.7,
+    repetition_penalty=1.3,
+)
 
-    response = ""
+# Set up HuggingFace Pipeline
+llm_pipeline = pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    generation_config=generation_config,
+)
 
-    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
+llm = HuggingFacePipeline(pipeline=llm_pipeline)
 
-        response += token
-        yield response
+# Set up QA Chain
+qa_chain = RetrievalQA.from_chain_type(
+    llm=llm,
+    retriever=qdrant_vectorstore.as_retriever(search_kwargs={"k": 3}),
+    chain_type="stuff"
+)
 
+# Generate prompt based on language
+def generate_prompt(question):
+    lang = detect(question)
+    if lang == "ar":
+        return f"""أجب على السؤال الطبي التالي بلغة عربية فصحى، بإجابة دقيقة ومفصلة. إذا لم تجد معلومات كافية في السياق، اس��خدم معرفتك الطبية السابقة. 
+ وتأكد من ان:
+- عدم تكرار أي نقطة أو عبارة أو كلمة
+- وضوح وسلاسة كل نقطة
+- تجنب الحشو والعبارات الزائدة-
 
+السؤال: {question}
+الإجابة:
 """
-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)",
-        ),
-    ],
-)
+    else:
+        return f"""Answer the following medical question in clear English with a detailed, non-redundant response. Do not repeat ideas, phrases, or restate the question in the answer. If the context lacks relevant information, rely on your prior medical knowledge. If the answer involves multiple points, list them in concise and distinct bullet points:
+Question: {question}
+Answer:"""
+
+# Define Gradio interface function
+def medical_chatbot(question):
+    formatted_question = generate_prompt(question)
+    answer = qa_chain.run(formatted_question)
+    return answer
 
+# Set up Gradio interface
+iface = gr.Interface(
+    fn=medical_chatbot,
+    inputs=gr.Textbox(label="Ask a Medical Question", placeholder="Type your question here..."),
+    outputs=gr.Textbox(label="Answer", interactive=False),
+    title="Medical Chatbot",
+    description="Ask medical questions and get detailed answers in Arabic or English.",
+    theme="compact"
+)
 
+# Launch Gradio interface
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()