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README.md

@@ -1,12 +1,13 @@
 ---
-title: Tchap
-emoji: 📊
-colorFrom: purple
-colorTo: green
+title: Guillaume-Tell
+emoji: 📜
+colorFrom: gray
+colorTo: blue
 sdk: gradio
-sdk_version: 4.31.0
+sdk_version: 3.50.2
 app_file: app.py
 pinned: false
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import transformers
+import re
+from transformers import AutoConfig, AutoTokenizer, AutoModel, AutoModelForCausalLM
+from vllm import LLM, SamplingParams
+import torch
+import gradio as gr
+import json
+import os
+import shutil
+import requests
+import chromadb
+import pandas as pd
+from chromadb.config import Settings
+from chromadb.utils import embedding_functions
+
+device = "cuda:0"
+sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="intfloat/multilingual-e5-base", device = "cuda")
+client = chromadb.PersistentClient(path="mfs_vector")
+collection = client.get_collection(name="sp_expanded", embedding_function = sentence_transformer_ef)
+
+
+# Define the device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+#Define variables 
+temperature=0.2
+max_new_tokens=1000
+top_p=0.92
+repetition_penalty=1.7
+
+model_name = "AgentPublic/Guillaume-Tell"
+
+llm = LLM(model_name, max_model_len=4096)
+
+#Vector search over the database
+def vector_search(collection, text):
+
+    results = collection.query(
+        query_texts=[text],
+        n_results=5,
+    )
+
+    document = []
+    document_html = []
+    id_list = ""
+    list_elm = 0
+    for ids in results["ids"][0]:
+        first_link = str(results["metadatas"][0][list_elm]["identifier"])
+        first_title = results["documents"][0][list_elm]
+        list_elm = list_elm+1
+
+        document.append(first_link + " : " + first_title)
+        document_html.append('<div class="source" id="' + first_link + '"><p><b>' + first_link + "</b> : " + first_title + "</div>")
+
+    document = "\n\n".join(document)
+    document_html = '<div id="source_listing">' + "".join(document_html) + "</div>"
+    # Replace this with the actual implementation of the vector search
+    return document, document_html
+    
+#CSS for references formatting
+css = """
+.generation {
+    margin-left:2em;
+    margin-right:2em;
+    size:1.2em;
+}
+
+:target {
+    background-color: #CCF3DF; /* Change the text color to red */
+  }
+
+.source {
+    float:left;
+    max-width:17%;
+    margin-left:2%;
+}
+
+.tooltip {
+    position: relative;
+    cursor: pointer;
+    font-variant-position: super;
+    color: #97999b;
+  }
+  
+  .tooltip:hover::after {
+    content: attr(data-text);
+    position: absolute;
+    left: 0;
+    top: 120%; /* Adjust this value as needed to control the vertical spacing between the text and the tooltip */
+    white-space: pre-wrap; /* Allows the text to wrap */
+    width: 500px; /* Sets a fixed maximum width for the tooltip */
+    max-width: 500px; /* Ensures the tooltip does not exceed the maximum width */
+    z-index: 1;
+    background-color: #f9f9f9;
+    color: #000;
+    border: 1px solid #ddd;
+    border-radius: 5px;
+    padding: 5px;
+    display: block;
+    box-shadow: 0 4px 8px rgba(0,0,0,0.1); /* Optional: Adds a subtle shadow for better visibility */
+  }"""
+
+#Curtesy of chatgpt
+def format_references(text):
+    # Define start and end markers for the reference
+    ref_start_marker = '<ref text="'
+    ref_end_marker = '</ref>'
+    
+    # Initialize an empty list to hold parts of the text
+    parts = []
+    current_pos = 0
+    ref_number = 1
+    
+    # Loop until no more reference start markers are found
+    while True:
+        start_pos = text.find(ref_start_marker, current_pos)
+        if start_pos == -1:
+            # No more references found, add the rest of the text
+            parts.append(text[current_pos:])
+            break
+        
+        # Add text up to the start of the reference
+        parts.append(text[current_pos:start_pos])
+        
+        # Find the end of the reference text attribute
+        end_pos = text.find('">', start_pos)
+        if end_pos == -1:
+            # Malformed reference, break to avoid infinite loop
+            break
+        
+        # Extract the reference text
+        ref_text = text[start_pos + len(ref_start_marker):end_pos].replace('\n', ' ').strip()
+        ref_text_encoded = ref_text.replace("&", "&amp;").replace("<", "&lt;").replace(">", "&gt;")
+        
+        # Find the end of the reference tag
+        ref_end_pos = text.find(ref_end_marker, end_pos)
+        if ref_end_pos == -1:
+            # Malformed reference, break to avoid infinite loop
+            break
+        
+        # Extract the reference ID
+        ref_id = text[end_pos + 2:ref_end_pos].strip()
+        
+        # Create the HTML for the tooltip
+        tooltip_html = f'<span class="tooltip" data-refid="{ref_id}" data-text="{ref_id}: {ref_text_encoded}"><a href="#{ref_id}">[' + str(ref_number) +']</a></span>'
+        parts.append(tooltip_html)
+        
+        # Update current_pos to the end of the current reference
+        current_pos = ref_end_pos + len(ref_end_marker)
+        ref_number = ref_number + 1
+    
+    # Join and return the parts
+    parts = ''.join(parts)
+
+    return parts
+
+# Class to encapsulate the Falcon chatbot
+class MistralChatBot:
+    def __init__(self, system_prompt="Le dialogue suivant est une conversation"):
+        self.system_prompt = system_prompt
+
+    def predict(self, user_message):
+        fiches, fiches_html = vector_search(collection, user_message)
+        sampling_params = SamplingParams(temperature=.7, top_p=.95, max_tokens=2000, presence_penalty = 1.5, stop = ["``"])
+        detailed_prompt = """<|im_start|>system
+        Tu es Albert, le chatbot des Maisons France Service qui donne des réponses sourcées.<|im_end|>
+        <|im_start|>user
+        Ecrit un texte référencé en réponse à cette question : """ + user_message + """
+
+        Les références doivent être citées de cette manière : texte rédigé<ref text=\"[passage pertinent dans la référence]\">[\"identifiant de la référence\"]</ref>Si les références ne permettent pas de répondre, qu'il n'y a pas de réponse.
+
+        Les cinq références disponibles : """ + fiches + "<|im_end|>\n<|im_start|>assistant\n"
+        print(detailed_prompt)
+        prompts = [detailed_prompt]
+        outputs = llm.generate(prompts, sampling_params, use_tqdm = False)
+        generated_text = outputs[0].outputs[0].text
+        generated_text = '<h2 style="text-align:center">Réponse</h3>\n<div class="generation">' + format_references(generated_text) + "</div>"
+        fiches_html = '<h2 style="text-align:center">Sources</h3>\n' + fiches_html
+        return generated_text, fiches_html
+
+# Create the Falcon chatbot instance
+mistral_bot = MistralChatBot()
+
+# Define the Gradio interface
+title = "Guillaume-Tell"
+description = "Le LLM répond à des questions administratives sur l'éducation nationale à partir de sources fiables."
+examples = [
+    [
+        "Qui peut bénéficier de l'AIP?",  # user_message
+        0.7  # temperature
+    ]
+]
+
+additional_inputs=[
+    gr.Slider(
+        label="Température",
+        value=0.2,  # Default value
+        minimum=0.05,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Des valeurs plus élevées donne plus de créativité, mais aussi d'étrangeté",
+    ),
+]
+
+demo = gr.Blocks()
+
+with gr.Blocks(theme='JohnSmith9982/small_and_pretty', css=css) as demo:
+    gr.HTML("""<h1 style="text-align:center">Albert (Guillaume-Tell)</h1>""")
+    text_input = gr.Textbox(label="Votre question ou votre instruction.", type="text", lines=1)
+    text_button = gr.Button("Interroger Albert")
+    text_output = gr.HTML(label="La réponse d'Albert")
+    embedding_output = gr.HTML(label="Les sources utilisées")
+    text_button.click(mistral_bot.predict, inputs=text_input, outputs=[text_output, embedding_output])
+
+if __name__ == "__main__":
+    demo.queue().launch()

gitattributes

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+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+education_database/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

requirements.txt

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+transformers
+torch
+einops
+accelerate
+tiktoken
+scipy
+transformers_stream_generator==0.0.4 
+peft 
+deepspeed
+bitsandbytes
+optimum
+vllm==0.3.2
+chromadb
+sentence_transformers

theme_builder.py

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+import gradio as gr
+
+gr.themes.builder()