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app (1).py

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+
+import gradio as gr
+import transformers
+import torch
+from PIL import Image
+
+# Chargement des modèles
+translator = transformers.pipeline("translation", model="facebook/nllb-200-distilled-600M")
+text_gen_pipeline = transformers.pipeline(
+    "text-generation",
+    model="ContactDoctor/Bio-Medical-Llama-3-2-1B-CoT-012025",
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Message système initial
+system_message = {
+    "role": "system",
+    "content": (
+        "You are a helpful, respectful, and knowledgeable medical assistant developed by the AI team at AfriAI Solutions, Senegal. "
+        "Provide brief, clear definitions when answering medical questions. After giving a concise response, ask the user if they would like more information about symptoms, causes, or treatments. "
+        "Always encourage users to consult healthcare professionals for personalized advice."
+    )
+}
+
+messages = [system_message]
+max_history = 10
+
+# Expressions reconnues
+salutations = ["bonjour", "salut", "bonsoir", "coucou"]
+remerciements = ["merci", "je vous remercie", "thanks"]
+au_revoir = ["au revoir", "à bientôt", "bye", "bonne journée", "à la prochaine"]
+
+def detect_smalltalk(user_input):
+    lower_input = user_input.lower().strip()
+
+    if any(phrase in lower_input for phrase in salutations):
+        return "Bonjour ! Comment puis-je vous aider aujourd'hui ?", True
+    if any(phrase in lower_input for phrase in remerciements):
+        return "Avec plaisir ! Souhaitez-vous poser une autre question médicale ?", True
+    if any(phrase in lower_input for phrase in au_revoir):
+        return "Au revoir ! Prenez soin de votre santé et n'hésitez pas à revenir si besoin.", True
+
+    return "", False
+
+def medical_chatbot(user_input):
+    global messages
+
+    # Gestion des interactions sociales
+    smalltalk_response, handled = detect_smalltalk(user_input)
+    if handled:
+        return smalltalk_response
+
+    # Traduction français -> anglais
+    translated = translator(user_input, src_lang="fra_Latn", tgt_lang="eng_Latn")[0]['translation_text']
+    
+    messages.append({"role": "user", "content": translated})
+    if len(messages) > max_history * 2:
+        messages = [system_message] + messages[-max_history * 2:]
+
+    # Préparation du prompt
+    prompt = text_gen_pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+
+    # Génération
+    response = text_gen_pipeline(
+        prompt,
+        max_new_tokens=512,
+        do_sample=True,
+        temperature=0.4,
+        top_k=150,
+        top_p=0.75,
+        eos_token_id=[
+            text_gen_pipeline.tokenizer.eos_token_id,
+            text_gen_pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+        ]
+    )
+    
+    output = response[0]['generated_text'][len(prompt):].strip()
+
+    # Traduction anglais -> français
+    translated_back = translator(output, src_lang="eng_Latn", tgt_lang="fra_Latn")[0]['translation_text']
+    
+    messages.append({"role": "assistant", "content": translated_back})
+    return translated_back
+
+# Chargement du logo
+logo = Image.open("AfriAI_Solutions.jpg")
+
+# Interface Gradio
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="indigo")) as demo:
+    with gr.Row():
+        gr.Image(value=logo, show_label=False, show_download_button=False, interactive=False, height=150)
+
+    gr.Markdown(
+        """
+        # 🤖 Chatbot Médical AfriAI Solutions
+        **Posez votre question médicale en français.**  
+        Le chatbot vous répondra brièvement et avec bienveillance, puis vous demandera si vous souhaitez plus de détails.
+        """,
+        elem_id="title"
+    )
+
+    chatbot = gr.Chatbot(label="Chat avec le Médecin Virtuel", height=400)
+    msg = gr.Textbox(label="Votre question", placeholder="Exemple : Quels sont les symptômes du paludisme ?")
+
+    clear = gr.Button("Effacer la conversation", variant="secondary")
+
+    def respond(message, history):
+        response = medical_chatbot(message)
+        history = history or []
+        history.append((message, response))
+        return "", history
+
+    msg.submit(respond, [msg, chatbot], [msg, chatbot])
+    clear.click(lambda: ("", []), None, [msg, chatbot])
+
+demo.launch()

requirements (2).txt

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+gradio>=4.0.0
+transformers>=4.38.0
+torch>=2.1.0
+pillow>=9.0.0