Update app.py

app.py

@@ -1,169 +1,51 @@
-import gradio as gr
-from langchain_community.vectorstores import Chroma
-from langchain_core.documents import Document
-from langchain_huggingface import HuggingFaceEmbeddings
-import json
+# -----------------------------
+# Imports
+# -----------------------------
+from langchain_openai import ChatOpenAI
+from langchain_core.caches import BaseCache  # Must be imported before model_rebuild
+from langchain.chains import LLMChain
+from langchain.prompts import PromptTemplate
 import os
 
-# -------------------------------
-# CONFIGURATION
-# -------------------------------
-os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", "sk-your-key-here")
-
-# -------------------------------
-# LOAD DATA AND BUILD RAG CHAIN
-# -------------------------------
-def clean_metadata(metadata):
-    """Convert list values to comma-separated strings for ChromaDB compatibility"""
-    cleaned = {}
-    for key, value in metadata.items():
-        if isinstance(value, list):
-            cleaned[key] = ", ".join(str(v) for v in value)
-        elif isinstance(value, (str, int, float, bool)) or value is None:
-            cleaned[key] = value
-        else:
-            cleaned[key] = str(value)
-    return cleaned
-
-print("Loading documents...")
-docs = []
-with open("helpwildlife_rag.jsonl", "r", encoding="utf-8") as f:
-    for line in f:
-        entry = json.loads(line)
-        metadata = entry.get("metadata", {})
-        docs.append(Document(
-            page_content=entry["text"],
-            metadata=clean_metadata(metadata)
-        ))
-print(f"✓ Loaded {len(docs)} documents")
-
-print("Loading embedding model...")
-embeddings = HuggingFaceEmbeddings(
-    model_name="sentence-transformers/all-MiniLM-L6-v2"
+# -----------------------------
+# Fix for "class not fully defined" Pydantic error
+# -----------------------------
+ChatOpenAI.model_rebuild()
+
+# -----------------------------
+# API key setup
+# -----------------------------
+# For local use: ensure OPENAI_API_KEY is set in your environment
+# For Hugging Face Space: add it under Settings → Secrets
+if "OPENAI_API_KEY" not in os.environ:
+    raise ValueError("OPENAI_API_KEY not found. Please set it in environment or Space secrets.")
+
+# -----------------------------
+# Initialise model
+# -----------------------------
+llm = ChatOpenAI(
+    model="gpt-4o-mini",  # or "gpt-4o", "gpt-4-turbo" etc.
+    temperature=0,
 )
 
-print("Building vector store...")
-try:
-    vectorstore = Chroma.from_documents(docs, embedding=embeddings)
-except TypeError:
-    vectorstore = Chroma.from_documents(docs, embedding_function=embeddings)
-
-retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-print("✓ Vector store created")
-
-# Prompt template as string
-prompt_template = """
-You are a compassionate wildlife advice assistant.
-Your only source of information is the text provided in the CONTEXT section below.
-Do not use outside knowledge, guesses, or general reasoning.
-If the context does not contain enough information to answer fully,
-say "I'm not sure based on the available guidance."
-
-Never suggest killing or harming any animal.
-Always emphasise that taking an injured or distressed animal to a local wildlife rescue
-or 24/7 animal hospital is the safest and most humane course of action.
-Discourage people from trying to handle or treat the animal themselves,
-and note that general veterinary clinics may euthanise wild animals unnecessarily.
-
-Adopt a voice of empathy and respect for all life, consistent with vegan principles:
-there are no animals that are pests, vermin, or unworthy of care.
-Respond clearly and calmly, with brief, practical, step-by-step guidance suitable for the public.
-
----------------------
-CONTEXT (from HelpWildlife data file):
-{context}
----------------------
-QUESTION:
-{question}
-
-YOUR ANSWER:
-"""
-
-# Global LLM variable - will be initialized lazily
-_llm = None
-
-def get_llm():
-    """Lazy load the LLM only when first needed"""
-    global _llm
-    if _llm is None:
-        from langchain_openai import ChatOpenAI
-        from langchain_core.prompts import ChatPromptTemplate
-        from langchain_core.output_parsers import StrOutputParser
-        from langchain_core.runnables import RunnablePassthrough
-
-        # --- FIX FOR PYDANTIC CLASS ISSUE ---
-        try:
-            ChatOpenAI.model_rebuild(force=True)
-        except Exception:
-            pass
-
-        llm = ChatOpenAI(model_name="gpt-4o-mini", temperature=0)
-        prompt = ChatPromptTemplate.from_template(prompt_template)
-
-        def format_docs(docs):
-            return "\n\n".join(doc.page_content for doc in docs)
-
-        _llm = (
-            {
-                "context": retriever | format_docs,
-                "question": RunnablePassthrough()
-            }
-            | prompt
-            | llm
-            | StrOutputParser()
-        )
-    return _llm
-
-print("✓ Setup complete - LLM will initialize on first use")
-
-# -------------------------------
-# GRADIO INTERFACE
-# -------------------------------
-def ask_wildlife_question(question):
-    """Process a wildlife question and return an answer"""
-    if not question.strip():
-        return "Please enter a question about wildlife."
-    
-    try:
-        rag_chain = get_llm()
-        answer = rag_chain.invoke(question)
-        return answer
-    except Exception as e:
-        return f"Error: {str(e)}\n\nPlease check your OpenAI API key is set correctly in Space secrets."
-
-# Example questions
-examples = [
-    "I found a baby hedgehog out during the day. What should I do?",
-    "There's a bird that seems injured in my garden. How can I help?",
-    "I found a baby bird on the ground. Should I put it back in the nest?",
-    "A fox is limping in my backyard. What should I do?",
-    "How do I know if a wild animal needs help?"
-]
+# -----------------------------
+# Example prompt and chain
+# -----------------------------
+prompt = PromptTemplate(
+    input_variables=["topic"],
+    template="Summarise the main challenges and opportunities of using AI in {topic}."
+)
 
-# Create Gradio interface
-demo = gr.Interface(
-    fn=ask_wildlife_question,
-    inputs=gr.Textbox(
-        label="Ask a Wildlife Question",
-        placeholder="e.g., I found a baby bird on the ground...",
-        lines=3
-    ),
-    outputs=gr.Textbox(
-        label="Compassionate Advice",
-        lines=10
-    ),
-    title="🦔 Wildlife Rescue Assistant",
-    description="""
-    Ask questions about helping wildlife in distress. This assistant provides compassionate, 
-    evidence-based advice prioritizing the wellbeing of all animals.
-    
-    ⚠️ **Important**: This tool provides general guidance only. For urgent situations, 
-    contact your local wildlife rescue or 24/7 animal hospital immediately.
-    """,
-    examples=examples,
-    theme=gr.themes.Soft(),
-    allow_flagging="never"
+chain = LLMChain(
+    llm=llm,
+    prompt=prompt
 )
 
+# -----------------------------
+# Run the chain
+# -----------------------------
 if __name__ == "__main__":
-    demo.launch()
+    topic = "government recordkeeping"
+    result = chain.run(topic=topic)
+    print("=== Model Output ===")
+    print(result)