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from transformers import AutoModelForCausalLM, AutoTokenizer
import gradio as gr
# --- 1. Load Roadmap and Rules ---
def load_roadmap_and_rules(roadmap_path="roadmap.txt", rules_path="rules.txt"):
roadmap_content = {}
with open(roadmap_path, 'r') as f:
current_section = None
for line in f:
line = line.strip()
if line.startswith("#"):
current_section = line[1:].strip()
roadmap_content[current_section] = ""
elif current_section:
roadmap_content[current_section] += line + "\n"
with open(rules_path, 'r') as f:
rules_content = f.read()
return roadmap_content, rules_content
roadmap, rules = load_roadmap_and_rules()
# --- 2. Load the AI Model (Mistral 7B) ---
model_name = "mistralai/Mistral-7B-Instruct-v0.2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# --- 3. Function to Get Chatbot Response ---
def get_chatbot_response(user_query, project_phase, roadmap, rules, model, tokenizer):
phase_roadmap_section = roadmap.get(project_phase, "General Guidance")
context = f"""
Project Roadmap (Phase: {project_phase}):
{phase_roadmap_section}
Project Rules:
{rules}
User Query: {user_query}
---
Provide helpful guidance for this project.
"""
prompt = f"<s>[INST] {context} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
response_start_index = response.find("[/INST]") + len("[/INST]") if "[/INST]" in response else 0
cleaned_response = response[response_start_index:].strip()
return cleaned_response
# --- 4. Gradio Interface Function ---
def chatbot_interface(user_query, project_phase):
return get_chatbot_response(user_query, project_phase, roadmap, rules, model, tokenizer)
# --- 5. Gradio Interface Setup ---
iface = gr.Interface(
fn=chatbot_interface,
inputs=[
gr.Textbox(label="Your Question"),
gr.Dropdown(list(roadmap.keys()), label="Project Phase", value=list(roadmap.keys())[0])
],
outputs="text",
title="Project Guidance Chatbot",
description="Ask questions about your project phase and get guidance."
)
if __name__ == "__main__":
iface.launch() |