Create app.py

app.py

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+import streamlit as st
+import json
+from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
+from langchain_groq import ChatGroq
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.prompts import ChatPromptTemplate, PromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
+
+# -----------------------------
+# Parsers
+# -----------------------------
+str_parser = StrOutputParser()
+json_parser = JsonOutputParser()
+
+# -----------------------------
+# API KEY INPUT (for Hugging Face Spaces)
+# -----------------------------
+api_key = st.text_input("Enter GROQ API Key", type="password")
+
+# -----------------------------
+# PROMPTS (UNCHANGED)
+# -----------------------------
+thought_prompt = ChatPromptTemplate.from_messages([
+    SystemMessagePromptTemplate.from_template("You are a strict reasoning AI that follows policy provided exactly"),
+    HumanMessagePromptTemplate.from_template("""
+Query:{query}
+Policy:{policy}
+
+Generate 3 different tree of reasoning paths.
+
+Rules:
+- Use ONLY the given policy
+- Do NOT assume anything
+- Each path must lead to a conclusion
+
+Return JSON:
+{{
+  "thoughts": [
+    {{"path": "Path 1", "reasoning": "...", "conclusion": "..."}},
+    {{"path": "Path 2", "reasoning": "...", "conclusion": "..."}},
+    {{"path": "Path 3", "reasoning": "...", "conclusion": "..."}}
+  ]
+}}
+""")
+])
+
+best_path_prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a strict evaluator that selects exactly ONE best answer based on policy and relevance."),
+    ("human", """
+Query: "{query}"
+Policy: "{policy}"
+
+Thoughts:
+{thoughts}
+
+Instructions:
+1. Evaluate ALL reasoning paths.
+2. First, eliminate any path that violates policy.
+3. If multiple paths are policy-compliant:
+   - Compare them based on:
+     a) Direct relevance to the query
+     b) Completeness of reasoning
+     c) Clarity and specificity
+4. You MUST select ONLY ONE best answer.
+5. Do NOT return multiple answers.
+6. Do NOT say "all are correct".
+
+Final Rule:
+Even if all options are correct, pick the MOST relevant and precise one.
+
+Return JSON:
+{{
+  "answer": "...",
+  "reason": "Explain why this was chosen over others"
+}}
+""")
+])
+
+verify_prompt = ChatPromptTemplate.from_messages([
+    ("system", "You are a strict policy verifier. Reject anything that violates policy."),
+    ("human", """
+Query: "{query}"
+Policy: "{policy}"
+
+Answer: "{answer}"
+Reason: "{reason}"
+
+Verification Rules:
+- Must strictly follow policy
+- No assumptions allowed
+- Must be logically consistent
+
+Return JSON:
+{{
+  "verified": true/false,
+  "confidence": "high/medium/low",
+  "final_answer": "..."
+}}
+""")
+])
+
+# -----------------------------
+# Streamlit UI
+# -----------------------------
+
+st.title("🛒 Amazon Customer Support AI")
+
+query = st.text_area("Enter Query")
+policy = st.text_area("Enter Policy")
+
+if st.button("Run"):
+    if not api_key:
+        st.warning("Please enter GROQ API Key")
+    elif not query or not policy:
+        st.warning("Please enter both query and policy")
+    else:
+        llm = ChatGroq(model="openai/gpt-oss-120b", api_key=api_key)
+
+        # Chains (UNCHANGED LOGIC)
+        final_thought_chain = thought_prompt | llm | json_parser
+        final_bp = best_path_prompt | llm | json_parser
+        final_verification = verify_prompt | llm | json_parser
+
+        final_chain = (
+            RunnablePassthrough.assign(
+                thoughts=final_thought_chain
+            )
+            | RunnablePassthrough.assign(
+                bp_output=final_bp
+            )
+            | {
+                "query": lambda x: x["query"],
+                "policy": lambda x: x["policy"],
+                "answer": lambda x: x["bp_output"]["answer"],
+                "reason": lambda x: x["bp_output"]["reason"],
+            }
+            | final_verification
+        )
+
+        with st.spinner("Processing..."):
+            result = final_chain.invoke({"query": query, "policy": policy})
+
+        st.subheader("✅ Final Output")
+        st.json(result)
+
+# -----------------------------
+# Hugging Face Instructions
+# -----------------------------
+
+st.markdown("""
+---
+### 🚀 Deploy on Hugging Face Spaces
+
+1. Create Streamlit Space
+2. Upload this as app.py
+3. Add requirements.txt:
+
+```
+streamlit
+langchain
+langchain-groq
+```
+
+4. Add GROQ API key in UI while running
+""")