Refactor chat functionality and add system prompt for DevalBot

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
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+-----END CERTIFICATE-----

app copy.py

@@ -0,0 +1,89 @@
+import gradio as gr
+from bedrock_client import claude_llm, get_anthropic_client, claude_stream_response
+from utils import load_users
+
+AUTHS = load_users('user.csv')
+HISTORY_LIMIT = 30  # max number of turns (user+assistant) to keep
+
+
+# 1) Your system prompt
+SYSTEM_PROMPT = (
+    "Du bist DevalBot, ein konversationeller Assistent des Deutschen Evaluierungsinstituts für Entwicklungsbewertung (DEval).  DEval bietet staatlichen und zivilgesellschaftlichen Organisationen in der Entwicklungszusammenarbeit unabhängige und wissenschaftlich fundierte Evaluierungen. Deine Hauptsprache ist Deutsch; antworte daher standardmäßig auf Deutsch. Du kannst zudem bei statistischen Analysen und Programmierung in Stata und R unterstützen. Antworte sachlich, präzise und stelle bei Unklarheiten klärende Rückfragen."
+)
+
+def chat(user_message, history):
+    # ── 1) Guard against empty input ─────────────────────
+    if not user_message or not user_message.strip():
+        return
+
+    # ── 2) Build the LLM’s messages list ─────────────────
+    # Always start with the SYSTEM_PROMPT, then the UI history,
+    # then the new user turn:
+    llm_messages = [{"role":"system","content":SYSTEM_PROMPT}]
+    llm_messages += history
+    llm_messages.append({"role":"user","content":user_message})
+
+    # ── 3) Kick off the streaming call ───────────────────
+    client   = get_anthropic_client()
+    streamer = lambda msgs: claude_stream_response(msgs, client)
+
+    # ── 4) Immediately show the user’s turn in the UI      ─
+    ui_history = history + [{"role":"user","content":user_message}]
+
+    full_resp = ""
+    try:
+        for chunk in streamer(llm_messages):
+            full_resp += chunk
+            # yield the UI history plus the growing assistant bubble
+            yield ui_history + [{"role":"assistant","content": full_resp}]
+    except Exception as e:
+        # surface any error inline
+        err = f"⚠️ Oops, something went wrong: {e}"
+        yield ui_history + [{"role":"assistant","content": err}]
+        return
+
+    # ── 5) Finalize the assistant turn in the UI ─────────
+    ui_history.append({"role":"assistant","content": full_resp})
+
+    # ── 6) Trim to the last N turns ──────────────────────
+    if len(ui_history) > HISTORY_LIMIT:
+        ui_history = ui_history[-HISTORY_LIMIT:]
+
+    yield ui_history
+
+
+with gr.Blocks(css_paths=["static/deval.css"],theme = gr.themes.Default(primary_hue="blue", secondary_hue="yellow"),) as demo:
+    # ── Logo + Header + Logout ────────────────────────────────
+
+    gr.Image(
+        value="static/logo.png",
+        show_label=False,
+        interactive=False,
+        show_download_button=False,
+        show_fullscreen_button=False,
+        elem_id="logo-primary",    # matches the CSS above
+    )
+        
+    #logout_btn = gr.Button("Logout", elem_id="logout-btn")
+    # inject auto-reload script
+    gr.HTML(
+        """
+        <script>
+          // Reload the page after 1 minutes (300 000 ms)
+          setTimeout(() => {
+            window.location.reload();
+          }, 1000);
+        </script>
+        """
+    )
+    gr.ChatInterface(
+        chat,
+        type="messages",
+        editable=True,
+        concurrency_limit=200,
+        save_history=True,
+    )
+    
+
+
+demo.queue().launch(auth=AUTHS, ssr_mode=False)

bedrock_test.py

@@ -7,7 +7,7 @@ import json
 import logging
 aws_access_key_id='AKIA2UC26WDCAFOF7X64'
 aws_secret_access_key='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'
-aws_region = "eu-central-1"
+aws_region = "eu-north-1"
 #%%
 from anthropic import AnthropicBedrock
 
@@ -24,91 +24,14 @@ client = AnthropicBedrock(
     aws_region=aws_region,
 )
 modelid='anthropic.claude-v2:1'
+model_id='eu.anthropic.claude-3-7-sonnet-20250219-v1:0'
 message = client.messages.create(
     model=modelid,
     max_tokens=256,
     messages=[{"role": "user", "content": "Hello, world"}]
 )
 print(message.content)
-#%%
-
-# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
-# SPDX-License-Identifier: Apache-2.0
-"""
-Shows how to generate a message with Anthropic Claude (on demand).
-"""
-import boto3
-import json
-import logging
-
-from botocore.exceptions import ClientError
-
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(level=logging.INFO)
-
-def generate_message(bedrock_runtime, model_id, system_prompt, messages, max_tokens):
-
-    body=json.dumps(
-        {
-            "anthropic_version": "bedrock-2023-05-31",
-            "max_tokens": max_tokens,
-            "system": system_prompt,
-            "messages": messages
-        }  
-    )  
-
-    
-    response = bedrock_runtime.invoke_model(body=body, modelId=model_id)
-    response_body = json.loads(response.get('body').read())
-   
-    return response_body
-
-def main():
-    """
-    Entrypoint for Anthropic Claude message example.
-    """
-
-    try:
-        # Define your credentials here (temporary for dev/testing only)
-
-        aws_region = "eu-central-1"  # or the correct region
-
-        # Use boto3 session to inject credentials
-        session = boto3.Session(
-            aws_access_key_id=aws_access_key_id,
-            aws_secret_access_key=aws_secret_access_key,
-            region_name=aws_region
-        )
-
-        bedrock_runtime = session.client(service_name='bedrock-runtime')
-
-        model_id = 'anthropic.claude-v2:1'
-        system_prompt = "Please respond only with emoji."
-        max_tokens = 1000
-
-        # Prompt with user turn only.
-        user_message = {"role": "user", "content": "Hello World"}
-        messages = [user_message]
-
-        response = generate_message(bedrock_runtime, model_id, system_prompt, messages, max_tokens)
-        print("User turn only.")
-        print(json.dumps(response, indent=4))
-
-        # Prompt with both user and assistant messages
-        assistant_message = {"role": "assistant", "content": "<emoji>"}
-        messages = [user_message, assistant_message]
-        response = generate_message(bedrock_runtime, model_id, system_prompt, messages, max_tokens)
-        print("User turn and prefilled assistant response.")
-        print(json.dumps(response, indent=4))
-
-    except ClientError as err:
-        message = err.response["Error"]["Message"]
-        logger.error("A client error occurred: %s", message)
-        print("A client error occurred: " + format(message))
-
 
-main()
 
 #%%
 from botocore.exceptions import ClientError
@@ -205,4 +128,88 @@ except ClientError as e:
         print(f"❌ Unexpected error: {e}")
 except Exception as e:
     print(f"❌ General error: {e}")
+#%%
+
+
+AWS_ACCESS_KEY_ID='AKIA2UC26WDCAFOF7X64'
+AWS_SECRET_ACCESS_KEY='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'
+aws_region = "eu-west-1"
+
+
+#%%
+AWS_ACCESS_KEY_ID='AKIA2UC26WDCAFOF7X64'
+AWS_SECRET_ACCESS_KEY='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'
+aws_region = "eu-west-1"
+
+from langchain_aws import BedrockLLM
+modelid='anthropic.claude-v2:1'
+modelid="mistral.mistral-large-2402-v1:0"
+#model_id='eu.anthropic.claude-3-7-sonnet-20250219-v1:0'
+custom_llm = BedrockLLM(
+    aws_access_key_id     = AWS_ACCESS_KEY_ID,
+    aws_secret_access_key = AWS_SECRET_ACCESS_KEY,
+
+    region_name           = aws_region,
+
+    # which Bedrock “provider” you’re talking to:
+    # – use "anthropic" for Claude models
+    # – use "cohere" for the Cohere models
+    provider              = "mistral",
+
+    model_id              = modelid,
+    model_kwargs          = {"temperature": 0.7},
+    streaming             = True,
+)
+
+print(custom_llm.invoke("What’s the recipe for mayonnaise?"))
+
+#%%
+from langchain_aws import ChatBedrockConverse
+import os
+system_prompt = (
+    "Du bist DevalBot, ein konversationeller Assistent des Deutschen Evaluierungsinstituts "
+    "für Entwicklungsbewertung (DEval). DEval bietet staatlichen und zivilgesellschaftlichen "
+    "Organisationen in der Entwicklungszusammenarbeit unabhängige und wissenschaftlich fundierte "
+    "Evaluierungen. Deine Hauptsprache ist Deutsch; antworte daher standardmäßig auf Deutsch. "
+    "Du kannst zudem bei statistischen Analysen und Programmierung in Stata und R unterstützen."
+)
+#%%
+# Initialize the streaming Bedrock chat model
+bedrock_llm = ChatBedrockConverse(
+    aws_access_key_id=AWS_ACCESS_KEY_ID,
+    aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
+    region_name=os.environ.get("AWS_DEFAULT_REGION", "eu-west-1"),
+    model_id="mistral.mistral-large-2402-v1:0",  # or your preferred Bedrock model
+    temperature= 0.7)
+#%%
+from bedrock_client import bedrock_llm, system_prompt
+from langchain.schema import SystemMessage, HumanMessage, AIMessage
+
+
+
+def build_messages(
+                   user_message: str,
+                   history: list[dict]) -> list:
+
+    messages: list = []
+    
+    # 1) Add the system prompt first
+    messages.append(SystemMessage(content=system_prompt))
+    
+    # 2) Walk the history and map to HumanMessage or AIMessage
+    for msg in history:
+        if msg["role"] == "user":
+            messages.append(HumanMessage(content=msg["content"]))
+        elif msg["role"] == "assistant":
+            messages.append(AIMessage(content=msg["content"]))
+        else:
+            # you can choose to ignore or log unexpected roles
+            continue
+    
+    # 3) Finally, append the new user message
+    messages.append(HumanMessage(content=user_message))
+    return messages
+
+
+build_messages('hi',[])
 #%%

test copy.py

@@ -0,0 +1,40 @@
+#%%
+from langchain.schema import SystemMessage, HumanMessage, AIMessage
+
+
+# Define your system prompt as a plain string
+
+
+
+def build_messages(
+                   user_message: str,
+                   history: list[dict]) -> list:
+
+    system_prompt = "Du bist DevalBot, ein konversationeller Assistent des Deutschen Evaluierungsinstituts " \
+    "für Entwicklungsbewertung (DEval). DEval bietet staatlichen und zivilgesellschaftlichen " \
+    "Organisationen in der Entwicklungszusammenarbeit unabhängige und wissenschaftlich fundierte " \
+    "Evaluierungen. Deine Hauptsprache ist Deutsch; antworte daher standardmäßig auf Deutsch. " \
+    "Du kannst zudem bei statistischen Analysen und Programmierung in Stata und R unterstützen."
+
+    messages: list = []
+    
+    # 1) Add the system prompt first
+    messages.append(SystemMessage(content=system_prompt))
+    
+    # 2) Walk the history and map to HumanMessage or AIMessage
+    for msg in history:
+        if msg["role"] == "user":
+            messages.append(HumanMessage(content=msg["content"]))
+        elif msg["role"] == "assistant":
+            messages.append(AIMessage(content=msg["content"]))
+        else:
+            # you can choose to ignore or log unexpected roles
+            continue
+    
+    # 3) Finally, append the new user message
+    messages.append(HumanMessage(content=user_message))
+    return messages
+
+
+build_messages('hi',[])
+#%%