Addressed quantization issue by enforcing fp16 precision for DeepSeek-V3 model loading. Updated error handling and improved compatibility for Multi-Agent XAI Demo.

app.py

@@ -1,134 +1,3 @@
-import streamlit as st
-
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-##############################################################################
-#                          LOAD MODELS
-##############################################################################
-
-@st.cache_resource
-def load_model_engineer():
-    try:
-        # Engineer: DeepSeek-V3 loaded directly
-        tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-V3", trust_remote_code=True)
-        model = AutoModelForCausalLM.from_pretrained(
-            "unsloth/DeepSeek-V3",
-            trust_remote_code=True
-        )
-        model = model.half().to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
-        return tokenizer, model
-    except Exception as e:
-        st.error(f"Direct model loading failed for Engineer: {str(e)}")
-        raise
-
-@st.cache_resource
-def load_model_analyst():
-    try:
-        # Analyst: DeepSeek-V3 loaded directly
-        tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-V3", trust_remote_code=True)
-        model = AutoModelForCausalLM.from_pretrained(
-            "unsloth/DeepSeek-V3",
-            trust_remote_code=True
-        )
-        model = model.half().to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
-        return tokenizer, model
-    except Exception as e:
-        st.error(f"Direct model loading failed for Analyst: {str(e)}")
-        raise
-
-# Load models
-try:
-    engineer_model = load_model_engineer()
-    analyst_model = load_model_analyst()
-except Exception as load_error:
-    st.stop()
-
-##############################################################################
-#                     ENGINEER / ANALYST GENERATION
-##############################################################################
-
-def generate_response(prompt, model, max_sentences=2):
-    """
-    Generate a concise response based on the provided prompt.
-    """
-    try:
-        tokenizer, model = model
-        inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-        outputs = model.generate(
-            inputs["input_ids"],
-            max_new_tokens=50,
-            temperature=0.6,
-            top_p=0.8,
-            pad_token_id=tokenizer.pad_token_id
-        )
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
-        # Limit to max_sentences by splitting and rejoining
-        return " ".join(response.split(".")[:max_sentences]) + "."
-    except Exception as gen_error:
-        st.error(f"Error during response generation: {str(gen_error)}")
-        return "[Error generating response]"
-
-def summarize_conversation(conversation):
-    """
-    Summarize the entire conversation to produce a cohesive and actionable plan.
-    """
-    summary = "### Final Plan\n"
-    key_points = []
-    for speaker, text in conversation:
-        if speaker == "Engineer" or speaker == "Analyst":
-            key_points.append(f"- {speaker}: {text}")
-    summary += "\n".join(key_points[-6:])  # Include only the last 3 turns each
-    summary += "\n\nThis collaborative plan integrates technical and analytical insights into an actionable framework."
-    return summary
-
-##############################################################################
-#                         STREAMLIT APP
-##############################################################################
-
-st.title("Multi-Agent System with XAI Demo")
-
-if "conversation" not in st.session_state:
-    st.session_state.conversation = []
-
-if "user_input" not in st.session_state:
-    st.session_state.user_input = ""
-
-st.text_area("Enter your query:", value=st.session_state.user_input, height=100, max_chars=None, key="user_input")
-
-if st.button("Generate Responses"):
-    if st.session_state.user_input.strip():
-        user_text = st.session_state.user_input
-        st.session_state.conversation = [("User", user_text)]  # Clear and restart conversation
-
-        engineer_prompt_base = f"Given the problem: {user_text}, provide a concise and actionable technical solution."
-        analyst_prompt_base = "Based on the engineer's suggestion, provide complementary data-driven recommendations."
-
-        for turn in range(3):
-            # Engineer generates a response
-            with st.spinner(f"Engineer is formulating response {turn + 1}..."):
-                engineer_resp = generate_response(
-                    prompt=engineer_prompt_base,
-                    model=engineer_model
-                )
-                st.session_state.conversation.append(("Engineer", engineer_resp))
-
-            # Display Engineer response
-            st.markdown(f"### Engineer Response ({turn + 1})\n{engineer_resp}")
-
-            # Analyst generates a response based on engineer's output
-            with st.spinner(f"Analyst is formulating response {turn + 1}..."):
-                analyst_resp = generate_response(
-                    prompt=f"Engineer suggested: {engineer_resp}. {analyst_prompt_base}",
-                    model=analyst_model
-                )
-                st.session_state.conversation.append(("Analyst", analyst_resp))
-
-            # Display Analyst response
-            st.markdown(f"### Analyst Response ({turn + 1})\n{analyst_resp}")
-
-        # Summarize the final plan
-        with st.spinner("Generating the final plan..."):
-            final_plan = summarize_conversation(st.session_state.conversation)
-            st.session_state.conversation.append(("Summary", final_plan))
-            st.markdown(final_plan)
+version https://git-lfs.github.com/spec/v1
+oid sha256:33a11bfc0175eafaba57084965d74562e60c395888489cfcd3b5af0a34a80a51
+size 5647