Create app.py

app.py

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+import streamlit as st
+import os
+from langchain_huggingface import HuggingFaceEndpoint
+from langchain.prompts import PromptTemplate, FewShotPromptTemplate
+
+# --- 1. UI Setup ---
+st.set_page_config(page_title="Prompting Demo", page_icon="🤖")
+st.title("Prompt Engineering Demo")
+st.markdown("Experiment with Zero-Shot, Few-Shot, and Chain of Thought.")
+
+# --- 2. Model Setup ---
+# On HF Spaces, you store keys in "Settings > Variables and Secrets"
+api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+if not api_token:
+    st.error("Please add your HUGGINGFACEHUB_API_TOKEN to Secrets.")
+    st.stop()
+
+repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
+llm = HuggingFaceEndpoint(repo_id=repo_id, temperature=0.7)
+
+# --- 3. Sidebar Selection ---
+option = st.sidebar.selectbox(
+    "Choose Prompting Technique",
+    ("Zero-Shot", "Single-Shot", "Few-Shot", "Chain of Thought")
+)
+
+user_input = st.text_input("Enter your query:", "How does a solar panel work?")
+
+# --- 4. Logic for each Technique ---
+if st.button("Generate Response"):
+    if option == "Zero-Shot":
+        prompt = PromptTemplate.from_template("{input}")
+        result = llm.invoke(prompt.format(input=user_input))
+
+    elif option == "Single-Shot" or option == "Few-Shot":
+        # Examples for sentiment analysis or translation
+        examples = [
+            {"input": "I am happy", "output": "Positive"},
+            {"input": "This is bad", "output": "Negative"}
+        ]
+        # Use only one example for single-shot
+        ex_to_use = examples[:1] if option == "Single-Shot" else examples
+        
+        example_prompt = PromptTemplate(input_variables=["input", "output"], template="Input: {input}\nOutput: {output}")
+        few_shot_prompt = FewShotPromptTemplate(
+            examples=ex_to_use,
+            example_prompt=example_prompt,
+            suffix="Input: {input}\nOutput:",
+            input_variables=["input"]
+        )
+        result = llm.invoke(few_shot_prompt.format(input=user_input))
+
+    elif option == "Chain of Thought":
+        cot_template = """
+        Question: If John has 5 pears and gives 2 to Mary, how many does he have?
+        Answer: Let's think step by step. John starts with 5. He gives 2 away. 5 - 2 = 3. The answer is 3.
+
+        Question: {input}
+        Answer: Let's think step by step."""
+        prompt = PromptTemplate.from_template(cot_template)
+        result = llm.invoke(prompt.format(input=user_input))
+
+    st.subheader(f"Result ({option})")
+    st.write(result)