Adding explanation & background & utils file

app.py

@@ -5,11 +5,14 @@ from haystack.nodes import EmbeddingRetriever
 import numpy as np
 import openai
 import os
-
+from datasets import load_dataset
+from datasets import Dataset
+import time
+from utils import is_climate_change_related, make_pairs, set_openai_api_key
 
 document_store = FAISSDocumentStore.load(
-    index_path=f"./documents/climate_gpt.faiss",
-    config_path=f"./documents/climate_gpt.json",
+    index_path="./documents/climate_gpt.faiss",
+    config_path="./documents/climate_gpt.json",
 )
 
 classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
@@ -22,22 +25,19 @@ dense = EmbeddingRetriever(
 )
 
 
-def is_climate_change_related(sentence: str) -> bool:
-    results = classifier(
-        sequences=sentence,
-        candidate_labels=["climate change related", "non climate change related"],
-    )
-    return results["labels"][np.argmax(results["scores"])] == "climate change related"
-
+def gen_conv(query: str, history=[system_template], ipcc=True):
+    """return (answer:str, history:list[dict], sources:str)
 
-def make_pairs(lst):
-    """from a list of even lenght, make tupple pairs"""
-    return [(lst[i], lst[i + 1]) for i in range(0, len(lst), 2)]
+    Args:
+        query (str): _description_
+        history (list, optional): _description_. Defaults to [system_template].
+        ipcc (bool, optional): _description_. Defaults to True.
 
+    Returns:
+        _type_: _description_
+    """
+    retrieve = ipcc and is_climate_change_related(query, classifier)
 
-def gen_conv(query: str, history=[system_template], ipcc=True):
-    """return (answer:str, history:list[dict], sources:str)"""
-    retrieve = ipcc and is_climate_change_related(query)
     sources = ""
     messages = history + [
         {"role": "user", "content": query},
@@ -68,55 +68,60 @@ def gen_conv(query: str, history=[system_template], ipcc=True):
             f"{d.meta['file_name']} Page {d.meta['page_number']}:\n{d.content}"
             for d in docs
         )
+    else:
+        sources = "No environmental report was used to provide this answer."
+
     messages.append({"role": "assistant", "content": answer})
     gradio_format = make_pairs([a["content"] for a in messages[1:]])
 
     return gradio_format, messages, sources
 
 
-def set_openai_api_key(text):
-    """Set the api key and return chain.
-    If no api_key, then None is returned.
-    """
-    openai.api_key = os.environ["api_key"]
-
-    if text.startswith("sk-") and len(text) > 10:
-        openai.api_key = text
-    return f"You're all set: this is your api key: {openai.api_key}"
+# Gradio
+css_code = ".gradio-container {background-image: url('file=background.png')}"
 
+with gr.Blocks(title="🌍 ClimateGPT Ekimetrics", css=css_code) as demo:
 
-# Gradio
-with gr.Blocks(title="Eki IPCC Explorer") as demo:
     openai.api_key = os.environ["api_key"]
-    gr.Markdown("# Climate GPT")
-    # with gr.Row():
-    #     gr.Markdown("First step: Add your OPENAI api key")
-    #     openai_api_key_textbox = gr.Textbox(
-    #         placeholder="Paste your OpenAI API key (sk-...) and hit Enter",
-    #         show_label=False,
-    #         lines=1,
-    #         type="password",
-    #     )
-
-    gr.Markdown("""# Ask me anything, I'm a climate expert""")
+    # gr.Markdown("# Climate GPT")
+    gr.Markdown("### Welcome to Climate GPT 🌍 ! ")
+    gr.Markdown(
+        """
+        Climate GPT is an interactive exploration tool designed to help you easily find relevant information based on  of Environmental reports such as IPCCs and ??.
+
+        IPCC is a United Nations body that assesses the science related to climate change, including its impacts and possible response options. The IPCC is considered the leading scientific authority on all things related to global climate change.
+    """
+    )
+    gr.Markdown(
+        "**How does it work:** This Chatbot is a combination of two technologies. FAISS search applied to a vast amount of scientific climate reports and TurboGPT to generate human-like text from the part of the document extracted from the database."
+    )
+    gr.Markdown(
+        "⚠️ Warning: Always refer to the source (on the right side) to ensure the validity of the information communicated"
+    )
+    # gr.Markdown("""### Ask me anything, I'm a climate expert""")
     with gr.Row():
         with gr.Column(scale=2):
-            chatbot = gr.Chatbot()
+            chatbot = gr.Chatbot(css=".gradio-container {background-color: blue}")
             state = gr.State([system_template])
 
             with gr.Row():
                 ask = gr.Textbox(
-                    show_label=False, placeholder="Enter text and press enter"
+                    show_label=False,
+                    placeholder="Enter text and press enter",
+                    sample_inputs=["which country polutes the most ?"],
                 ).style(container=False)
+                print(f"Type from ask textbox {ask.type}")
 
         with gr.Column(scale=1, variant="panel"):
-
             gr.Markdown("### Sources")
             sources_textbox = gr.Textbox(
                 interactive=False, show_label=False, max_lines=50
             )
+
     ask.submit(
-        fn=gen_conv, inputs=[ask, state], outputs=[chatbot, state, sources_textbox]
+        fn=gen_conv,
+        inputs=[ask, state],
+        outputs=[chatbot, state, sources_textbox],
     )
     with gr.Accordion("Add your personal openai api key", open=False):
         openai_api_key_textbox = gr.Textbox(

utils.py

@@ -0,0 +1,46 @@
+import numpy as np
+import openai
+import os
+
+
+def is_climate_change_related(sentence: str, classifier) -> bool:
+    """_summary_
+
+    Args:
+        sentence (str): _description_
+        classifier (_type_): _description_
+
+    Returns:
+        bool: _description_
+    """
+    results = classifier(
+        sequences=sentence,
+        candidate_labels=["climate change related", "non climate change related"],
+    )
+    print(f" ## Result from is climate change related {results}")
+    return results["labels"][np.argmax(results["scores"])] == "climate change related"
+
+
+def make_pairs(lst):
+    """From a list of even lenght, make tupple pairs
+    Args:
+        lst (list): _description_
+    Returns:
+        list: _description_
+    """
+    return [(lst[i], lst[i + 1]) for i in range(0, len(lst), 2)]
+
+
+def set_openai_api_key(text):
+    """Set the api key and return chain.If no api_key, then None is returned.
+    To do : add raise error & Warning message
+    Args:
+        text (str): openai api key
+    Returns:
+        str: Result of connection
+    """
+    openai.api_key = os.environ["api_key"]
+
+    if text.startswith("sk-") and len(text) > 10:
+        openai.api_key = text
+    return f"You're all set: this is your api key: {openai.api_key}"