Merge branch 'main' of https://huggingface.co/spaces/Mohamed284/AskNature_BioChat

.gitattributes

@@ -1,2 +1,4 @@
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 documents_v1_5c0c37d3cbc20e235eeec7cffd2d312f.pkl filter=lfs diff=lfs merge=lfs -text
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+faiss_index_5c0c37d3cbc20e235eeec7cffd2d312f/index.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -21,7 +21,7 @@ from collections import defaultdict
 import hashlib
 from tqdm import tqdm
 from dotenv import load_dotenv
-
+import pickle
 load_dotenv()
 
 # --- Configuration ---
@@ -216,17 +216,19 @@ class EnhancedRetriever:
 
 # --- Generation System ---
 SYSTEM_PROMPT = """
-**Biomimicry Expert Guidelines**
+**Expert Biomimicry Advisor**
 
-- Use only the provided AskNature context (e.g., Source, Application, Strategy, technical_concepts). If no context is given, note that you're using your own expertise.
-- When referencing facts, use numeric citations in square brackets (e.g., [1]). Do not include full URLs inline.
-- Bold all technical terms (e.g., **protein-based pigmentation**, **DNA-level fiber design**).
-- Provide a concise, expert answer that explains the innovation and its sustainability benefits.
-- End your response with a "References" section listing each URL with its citation number.
+- **Objective**: Your role is to provide expert-level insights on biomimicry by using the provided AskNature context. When context is unavailable, rely on general knowledge.
+- **Answer Precision**: Always use precise technical language and structure your response logically, emphasizing the relationship between biological concepts and innovation.
+- **References**: Use numeric citations (e.g., [1]) when referencing data points or studies, corresponding to the URLs or sources provided in the context.
+- **Content Formatting**: Bold technical terms for emphasis (e.g., **protein synthesis**, **ecosystem mimicry**).
+- **Conclusion**: Summarize the sustainability impacts of the discussed technologies or ideas. Highlight innovative aspects and benefits.
+- **References Section**: At the end of your response, list all cited sources with their corresponding numbers.
 
 Context: {context}
 """
 
+
 @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=2, max=20))
 def get_ai_response(query: str, context: str, model: str) -> str:
     result = ""  # Initialize the result variable
@@ -314,7 +316,15 @@ def generate_response(question: str, model: str) -> str:
         return "An error occurred processing your request."
 
 # --- Gradio Interface ---
-def chat_interface(question: str, history: List[Tuple[str, str]], model: str):
+# Define the mapping from display names to actual model identifiers
+model_mapping = {
+    "Gemini-2.0-Flash": "gemini-2.0-flash",
+    "Meta-llama-3-70b-instruct(GWDG)": "meta-llama-3-70b-instruct",
+    "llama3-70b-8192(Groq)": "llama3-70b-8192"
+}
+
+def chat_interface(question: str, history: List[Tuple[str, str]], display_model: str):
+    model = model_mapping.get(display_model, "gemini-2.0-flash")  # Default to Gemini if not found
     response = generate_response(question, model)
     return "", history + [(question, response)]
 
@@ -324,7 +334,7 @@ with gr.Blocks(title="AskNature BioRAG Expert", theme=gr.themes.Soft()) as demo:
         chatbot = gr.Chatbot(label="Dialogue History", height=500)
     with gr.Row():
         question = gr.Textbox(placeholder="Ask about biomimicry (e.g. 'How does Werewool use coral proteins to make fibers?')", label="Inquiry", scale=4)
-        model_selector = gr.Dropdown(choices=["gemini-2.0-flash", "meta-llama-3-70b-instruct(GWDG)", "llama3-70b-8192(Groq)"], label="Generation Model", value="gemini-2.0-flash")
+        model_selector = gr.Dropdown(choices=list(model_mapping.keys()), label="Generation Model", value="Gemini-2.0-Flash")
         clear_btn = gr.Button("Clear History", variant="secondary")
 
     gr.Markdown("""

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