revert default model, use additional environ variable to default it

streamlit_app.py

@@ -225,7 +225,9 @@ def play_old_messages():
     st.session_state['model'] = model = st.selectbox(
         "Model:",
         options=OPENAI_MODELS + list(OPEN_MODELS.keys()),
-        index=OPENAI_MODELS.index('gpt-3.5-turbo'),
+        index=(OPENAI_MODELS + list(OPEN_MODELS.keys())).index(
+            "zephyr-7b-beta") if "DEFAULT_MODEL" not in os.environ or not os.environ["DEFAULT_MODEL"] else (
+                OPENAI_MODELS + list(OPEN_MODELS.keys())).index(os.environ["DEFAULT_MODEL"]),
         placeholder="Select model",
         help="Select the LLM model:",
         disabled=st.session_state['doc_id'] is not None or st.session_state['uploaded']
@@ -313,8 +315,8 @@ def play_old_messages():
                            disabled=uploaded_file is not None)
     if chunk_size == -1:
         context_size = st.slider("Context size", 3, 20, value=10,
-                             help="Number of paragraphs to consider when answering a question",
-                             disabled=not uploaded_file)
+                                 help="Number of paragraphs to consider when answering a question",
+                                 disabled=not uploaded_file)
     else:
         context_size = st.slider("Context size", 3, 10, value=4,
                                  help="Number of chunks to consider when answering a question",
@@ -363,17 +365,20 @@ def play_old_messages():
 
     # timestamp = datetime.utcnow()
 
+
 def rgb_to_hex(rgb):
     return "#{:02x}{:02x}{:02x}".format(*rgb)
 
+
 def generate_color_gradient(num_elements):
     # Define warm and cold colors in RGB format
     warm_color = (255, 165, 0)  # Orange
-    cold_color = (0, 0, 255)    # Blue
+    cold_color = (0, 0, 255)  # Blue
 
     # Generate a linear gradient of colors
     color_gradient = [
-        rgb_to_hex(tuple(int(warm * (1 - i/num_elements) + cold * (i/num_elements)) for warm, cold in zip(warm_color, cold_color)))
+        rgb_to_hex(tuple(int(warm * (1 - i / num_elements) + cold * (i / num_elements)) for warm, cold in
+                         zip(warm_color, cold_color)))
         for i in range(num_elements)
     ]
 
@@ -427,7 +432,7 @@ def generate_color_gradient(num_elements):
                                                                                               context_size=context_size)
                 annotations = [
                     GrobidAggregationProcessor.box_to_dict(coo) for coo in [c.split(",") for coord in
-                    coordinates for c in coord]
+                                                                            coordinates for c in coord]
                 ]
                 gradients = generate_color_gradient(len(annotations))
                 for i, color in enumerate(gradients):
@@ -465,6 +470,7 @@ def generate_color_gradient(num_elements):
 with left_column:
     if st.session_state['binary']:
         if st.session_state['should_show_annotations']:
-            pdf_viewer(input=st.session_state['binary'], width=600, height=800, annotations=st.session_state['annotations'])
+            pdf_viewer(input=st.session_state['binary'], width=600, height=800,
+                       annotations=st.session_state['annotations'])
         else:
             pdf_viewer(input=st.session_state['binary'], width=600, height=800)