initial commit

.gitignore

@@ -0,0 +1 @@
+__pycache__

app.py

@@ -0,0 +1,33 @@
+import gradio as gr
+from llm import predict
+from llama_index.llms.mistralai import MistralAI
+from llama_index.core.llms import ChatMessage
+
+title = "Gaia Mistral Chat RAG PDF Demo"
+description = "Example of an assistant with Gradio, RAG from PDF documents and Mistral AI via its API"
+placeholder = (
+    "Vous pouvez me posez une question sur ce contexte, appuyer sur Entrée pour valider"
+)
+placeholder_url = "Extract text from this url"
+llm_model = "mistral-medium"
+
+env_api_key = "Yb2kAF0DR4Mva5AEmoYFV3kYRAKdXB7i"
+query_engine = None
+
+  
+llm = MistralAI(api_key=env_api_key, model=llm_model)
+
+def predict(message, history):
+    messages = []
+    for human, assistant in history:
+        ChatMessage(role="system", content=assistant),
+        ChatMessage(role="user", content=human),
+        
+    messages.append(ChatMessage(role="user", content=message))
+    response = llm.stream_chat(messages)
+    partial_message = ""
+    for chunk in response:
+        partial_message = partial_message + chunk.delta
+        yield partial_message
+
+chat = gr.ChatInterface(predict).launch()

requirements.txt

@@ -0,0 +1,10 @@
+pypdf
+mistralai
+llama-index
+llama-index-llms-mistralai
+chromadb
+fastapi
+uvicorn
+gradio
+gradio_client
+starlette