gemini

app.py

@@ -184,14 +184,14 @@ with gr.Blocks(css_paths="style.css") as demo:
                 wrap=True,
             )
 
-        with gr.Tab("Edit papers", id="tab-pr"):
-            pr_paper_central_tab(paper_central_df.df_raw)
-
-        with gr.Tab("Leaderboards", id="tab-leaderboards"):
-            with gr.Tab("Authors"):
-                author_leaderboard_tab()
-            with gr.Tab("Contributors"):
-                author_resource_leaderboard_tab()
+        # with gr.Tab("Edit papers", id="tab-pr"):
+        #     pr_paper_central_tab(paper_central_df.df_raw)
+        #
+        # with gr.Tab("Leaderboards", id="tab-leaderboards"):
+        #     with gr.Tab("Authors"):
+        #         author_leaderboard_tab()
+        #     with gr.Tab("Contributors"):
+        #         author_resource_leaderboard_tab()
 
 
     # chat with paper

paper_chat_tab.py

@@ -1,3 +1,5 @@
+import google.generativeai as genai
+
 import gradio as gr
 from PyPDF2 import PdfReader
 from bs4 import BeautifulSoup
@@ -17,6 +19,19 @@ tokenizer_cache = {}
 
 # Global variables for providers
 PROVIDERS = {
+    "Gemini": {
+        "name": "Gemini",
+        "logo": "https://www.gstatic.com/lamda/images/gemini_thumbnail_c362e5eadc46ca9f617e2.png",
+        "endpoint": "https://example-gemini-endpoint",  # not need
+        # Not necessarily needed for Gemini since we use google.generativeai directly
+        "api_key_env_var": "GEMINI_API_KEY",  # If using env vars for key storage
+        "models": [
+            "gemini-2.0-flash-exp",
+            "gemini-1.5-flash",
+        ],
+        "type": "tuples",
+        "max_total_tokens": "50000",
+    },
     "SambaNova": {
         "name": "SambaNova",
         "logo": "https://venturebeat.com/wp-content/uploads/2020/02/SambaNovaLogo_H_F.jpg",
@@ -24,6 +39,7 @@ PROVIDERS = {
         "api_key_env_var": "SAMBANOVA_API_KEY",
         "models": [
             "Meta-Llama-3.1-70B-Instruct",
+            "Meta-Llama-3.3-70B-Instruct",
         ],
         "type": "tuples",
         "max_total_tokens": "50000",
@@ -191,6 +207,7 @@ def create_chat_interface(provider_dropdown, model_dropdown, paper_content, hf_t
         message_tokens_list = []
         total_tokens = context_token_length
 
+        # Reconstruct the conversation from history and current user message
         for user_msg, assistant_msg in history:
             user_tokens = tokenizer.encode(user_msg)
             messages.append({"role": "user", "content": user_msg})
@@ -208,6 +225,7 @@ def create_chat_interface(provider_dropdown, model_dropdown, paper_content, hf_t
         message_tokens_list.append(len(message_tokens))
         total_tokens += len(message_tokens)
 
+        # Token truncation logic
         if total_tokens > max_total_tokens:
             available_tokens = max_total_tokens - (total_tokens - context_token_length)
             if available_tokens > 0:
@@ -227,35 +245,67 @@ def create_chat_interface(provider_dropdown, model_dropdown, paper_content, hf_t
 
         final_messages = []
         if context:
-            final_messages.append({"role": "system", "content": f"{context}"})
+            final_messages.append(
+                {"role": "system" if not provider_name_value == "Gemini" else "user", "content": f"{context}"})
         final_messages.extend(messages)
 
         api_key = hf_token_value or os.environ.get(api_key_env_var)
         if not api_key:
             raise ValueError("API token is not provided.")
 
-        client = OpenAI(
-            base_url=endpoint,
-            api_key=api_key,
-        )
-
-        try:
-            completion = client.chat.completions.create(
-                model=model_name_value,
-                messages=final_messages,
-                stream=True,
+        # Gemini logic
+        if provider_name_value == "Gemini":
+            import google.generativeai as genai
+            genai.configure(api_key=api_key)
+
+            # According to the docs, model should be instantiated with full model name, e.g. "models/gemini-1.5-flash"
+            # Ensure your PROVIDERS dict sets the model_name_value accordingly (e.g. "models/gemini-1.5-flash")
+            model = genai.GenerativeModel(model_name=model_name_value)
+
+            # Convert final_messages into Gemini's format:
+            # Gemini expects a list of messages: [{"role": "user"/"assistant"/"system", "parts": ["..."]}, ...]
+            gemini_messages = []
+            for m in final_messages:
+                gemini_messages.append({"role": m["role"], "parts": [m["content"]]})
+
+            # Now call generate_content with stream=True
+            try:
+                response = model.generate_content(gemini_messages, stream=True)
+                response_text = ""
+                for chunk in response:
+                    if chunk.text:
+                        response_text += chunk.text
+                        yield response_text
+            except Exception as ex:
+                yield f"Error calling Gemini: {ex}"
+        else:
+            # Default OpenAI-compatible logic
+            from openai import OpenAI
+            import openai
+            import json
+
+            client = OpenAI(
+                base_url=endpoint,
+                api_key=api_key,
             )
-            response_text = ""
-            for chunk in completion:
-                delta = chunk.choices[0].delta.content or ""
-                response_text += delta
-                yield response_text
-        except json.JSONDecodeError as e:
-            yield f"JSON decoding error: {e.msg}"
-        except openai.OpenAIError as openai_err:
-            yield f"OpenAI error: {openai_err}"
-        except Exception as ex:
-            yield f"Unexpected error: {ex}"
+
+            try:
+                completion = client.chat.completions.create(
+                    model=model_name_value,
+                    messages=final_messages,
+                    stream=True,
+                )
+                response_text = ""
+                for chunk in completion:
+                    delta = chunk.choices[0].delta.content or ""
+                    response_text += delta
+                    yield response_text
+            except json.JSONDecodeError as e:
+                yield f"JSON decoding error: {e.msg}"
+            except openai.OpenAIError as openai_err:
+                yield f"OpenAI error: {openai_err}"
+            except Exception as ex:
+                yield f"Unexpected error: {ex}"
 
     chatbot = gr.Chatbot(label="Chatbot", scale=1, height=800, autoscroll=True)
     chat_interface = gr.ChatInterface(
@@ -299,7 +349,7 @@ def paper_chat_tab(paper_id, paper_from, paper_central_df):
 
             # Add a textbox for user to enter a paper_id (arxiv_id)
             paper_id_input = gr.Textbox(
-                label="Or enter a 🤗 paper_id (arxiv_id) directly",
+                label="Or enter a 🤗 paper_id directly",
                 placeholder="e.g. 1234.56789"
             )
 

requirements.txt

@@ -7,6 +7,7 @@ PyPDF2
 transformers
 beautifulsoup4
 openai
+google-generativeai
 # Set the primary index URL to PyTorch's CPU wheels
 --index-url https://download.pytorch.org/whl/cpu