Update app.py

app.py

@@ -1,158 +1,158 @@
-import streamlit as st
-import os
-from groq import Groq
-
-# Streamlit page configuration
-st.set_page_config(layout="wide")
-
-# Supported models
-SUPPORTED_MODELS = {
-    "Llama 3.2 1B (Preview)": "llama-3.2-1b-preview",
-    "Llama 3 70B": "llama3-70b-8192",
-    "Llama 3 8B": "llama3-8b-8192",
-    "Llama 3.1 70B": "llama-3.1-70b-versatile",
-    "Llama 3.1 8B": "llama-3.1-8b-instant",
-    "Mixtral 8x7B": "mixtral-8x7b-32768",
-    "Gemma 2 9B": "gemma2-9b-it",
-    "LLaVA 1.5 7B": "llava-v1.5-7b-4096-preview",
-    "Llama 3.2 3B (Preview)": "llama-3.2-3b-preview",
-    "Llama 3.2 11B Vision (Preview)": "llama-3.2-11b-vision-preview"
-}
-
-MAX_TOKENS = 1000
-
-# Initialize Groq client with API key
-groq_api_key = os.getenv("GROQ_API_KEY")
-if not groq_api_key:
-    st.error("GROQ_API_KEY not found in environment variables. Please set it and restart the app.")
-    st.stop()
-
-client = Groq(api_key=groq_api_key)
-st.image("p1.png", width=300)
-st.sidebar.image("p2.png", width=200)
-
-def main():
-    st.title("Marketing tool App")
-    
-    # Sidebar settings
-    st.sidebar.header("Configuration")
-    model = st.sidebar.selectbox("Select LLM Model", list(SUPPORTED_MODELS.keys()))
-    temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.5)
-    output_size = st.sidebar.selectbox(
-        "Select Output Size",
-        ["1-3 word sentences", "2-5 word sentences", "3-7 word sentences", "5-9 word sentences", "6-11 word sentences"]
-    )
-    bullet_points = st.sidebar.checkbox("Output as Bullet Points", value=True)
-    humanize_text = st.sidebar.checkbox("Humanize Text")
-    display_final_answer = st.sidebar.checkbox("Display Process", value=True)
-    reduce_words = st.sidebar.checkbox("Reduce Word Count by 50%")  # New checkbox for reducing word count
-
-    # Clear and reset buttons in the sidebar
-    if st.sidebar.button("Clear Input Fields"):
-        st.session_state.system_prompt = "Create a revised [text] use 3-5 words concise and focused, Provide the output in short format plus in bullet points or a brief paragraph,  plus  offer 2-3 alternates - suggest areas for improvement. . list final answer in separate area"
-        st.session_state.user_query = ""
-
-    # Input fields for system prompt and query
-    default_prompt = "Create a revised [text] use 3-5 words concise and focused, Provide the output in short format plus in bullet points or a brief paragraph,  plus  offer 2-3 alternates - suggest areas for improvement. . list final answer in separate area"
-    system_prompt = st.text_area("System Prompt", value=st.session_state.get("system_prompt", default_prompt), key="system_prompt")
-    user_query = st.text_area("Enter Your Query", value=st.session_state.get("user_query", ""), key="user_query")
-    
-    if st.button("Submit"):
-        with st.spinner("Generating response..."):
-            response = query_groq(model, temperature, system_prompt, user_query, output_size, humanize_text, reduce_words)
-        
-        col1, col2 = st.columns(2)
-        
-        with col1:
-            st.write("### Detailed Information")
-            st.write("Model:", model)
-            st.write("Temperature:", temperature)
-            st.write("Output Size:", output_size)
-            st.write("Bullet Points:")
-            st.write(bullet_points)
-            st.write("Humanize Text:", humanize_text)
-            st.write("Display Final Answer:", display_final_answer)
-            st.write("System Prompt:", system_prompt)
-            st.write("User Query:", user_query)
-            if display_final_answer:
-                st.write("### Original Response")
-                st.text_area("Original Response", value=response, height=600)
-        
-        with col2:
-            if display_final_answer:
-                processed_response = process_response(response, output_size, bullet_points, humanize_text, reduce_words)
-                additional_text = "Please review the response carefully before proceeding."
-                st.write("### Processed Response with Review")
-                st.text_area(response, value=processed_response + "\n" + additional_text, height=200)
-            else:
-                st.write("### Output Response")
-                st.text(response)
-        
-def query_groq(model, temperature, system_prompt, user_query, output_size, humanize_text, reduce_words):
-    try:
-        completion = client.chat.completions.create(
-            model=SUPPORTED_MODELS[model],
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_query}
-            ],
-            temperature=temperature,
-            max_tokens=MAX_TOKENS
-        )
-        if not completion.choices:
-            return "Error: No choices in the completion response."
-        return completion.choices[0].message.content
-    except Exception as e:
-        return f"Error: {str(e)}"
-
-def process_response(text, output_size, bullet_points, humanize_text, reduce_words):
-    if reduce_words:
-        # Reduce word count by 50%
-        words = text.split()
-        text = " ".join(words[:len(words)//2])
-    
-    if output_size == "1-3 word sentences":
-        text = reduce_to_sentences(text, 1, 3)
-    elif output_size == "2-5 word sentences":
-        text = reduce_to_sentences(text, 2, 5)
-    elif output_size == "3-7 word sentences":
-        text = reduce_to_sentences(text, 3, 7)
-    elif output_size == "5-9 word sentences":
-        text = reduce_to_sentences(text, 5, 9)
-    elif output_size == "6-11 word sentences":
-        text = reduce_to_sentences(text, 6, 11)
-    
-    if bullet_points:
-        text = reduce_to_bullet_points(text, 1, 11)
-    
-    if humanize_text:
-        text = humanize(text)
-    
-    return text
-
-def reduce_to_bullet_points(text, min_words, max_words):
-    sentences = text.split('.')
-    bullet_points = []
-    for sentence in sentences:
-        words = sentence.strip().split()
-        if min_words <= len(words) <= max_words:
-            bullet_points.append(f"- {' '.join(words)}")
-    return '\n'.join(bullet_points)
-
-def reduce_to_sentences(text, min_words, max_words):
-    sentences = text.split('.')
-    filtered_sentences = []
-    for sentence in sentences:
-        words = sentence.strip().split()
-        if min_words <= len(words) <= max_words:
-            filtered_sentences.append(sentence.strip())
-    return ' '.join(filtered_sentences)
-
-def humanize(text):
-    # This can be replaced with a more sophisticated humanization logic as needed
-    return text.replace(". ", ". Let's consider this further. ")
-
-st.sidebar.info("build by dw")
-
-if __name__ == "__main__":
-    main()
+import streamlit as st
+import os
+from groq import Groq
+
+# Streamlit page configuration
+st.set_page_config(layout="wide")
+
+# Supported models
+SUPPORTED_MODELS = {
+    "Llama 3.2 1B (Preview)": "llama-3.2-1b-preview",
+    "Llama 3 70B": "llama3-70b-8192",
+    "Llama 3 8B": "llama3-8b-8192",
+    "Llama 3.1 70B": "llama-3.1-70b-versatile",
+    "Llama 3.1 8B": "llama-3.1-8b-instant",
+    "Mixtral 8x7B": "mixtral-8x7b-32768",
+    "Gemma 2 9B": "gemma2-9b-it",
+    "LLaVA 1.5 7B": "llava-v1.5-7b-4096-preview",
+    "Llama 3.2 3B (Preview)": "llama-3.2-3b-preview",
+    "Llama 3.2 11B Vision (Preview)": "llama-3.2-11b-vision-preview"
+}
+
+MAX_TOKENS = 1000
+
+# Initialize Groq client with API key
+groq_api_key = os.getenv("GROQ_API_KEY")
+if not groq_api_key:
+    st.error("GROQ_API_KEY not found in environment variables. Please set it and restart the app.")
+    st.stop()
+
+client = Groq(api_key=groq_api_key)
+st.image("p1.png", width=300)
+st.sidebar.image("p2.png", width=200)
+
+def main():
+    st.title("Marketing tool App")
+    
+    # Sidebar settings
+    st.sidebar.header("Configuration")
+    model = st.sidebar.selectbox("Select LLM Model", list(SUPPORTED_MODELS.keys()))
+    temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.5)
+    output_size = st.sidebar.selectbox(
+        "Select Output Size",
+        ["1-3 word sentences", "2-5 word sentences", "3-7 word sentences", "5-9 word sentences", "6-11 word sentences"]
+    )
+    bullet_points = st.sidebar.checkbox("Output as Bullet Points", value=True)
+    humanize_text = st.sidebar.checkbox("Humanize Text")
+    display_final_answer = st.sidebar.checkbox("Display Process")
+    reduce_words = st.sidebar.checkbox("Reduce Word Count by 50%")  # New checkbox for reducing word count
+
+    # Clear and reset buttons in the sidebar
+    if st.sidebar.button("Clear Input Fields"):
+        st.session_state.system_prompt = "Create a revised [text] use 3-5 words concise and focused, Provide the output in short format plus in bullet points or a brief paragraph,  plus  offer 2-3 alternates - suggest areas for improvement. . list final answer in separate area"
+        st.session_state.user_query = ""
+
+    # Input fields for system prompt and query
+    default_prompt = "Create a revised [text] use 3-5 words concise and focused, Provide the output in short format plus in bullet points or a brief paragraph,  plus  offer 2-3 alternates - suggest areas for improvement. . list final answer in separate area"
+    system_prompt = st.text_area("System Prompt", value=st.session_state.get("system_prompt", default_prompt), key="system_prompt")
+    user_query = st.text_area("Enter Your Query", value=st.session_state.get("user_query", ""), key="user_query")
+    
+    if st.button("Submit"):
+        with st.spinner("Generating response..."):
+            response = query_groq(model, temperature, system_prompt, user_query, output_size, humanize_text, reduce_words)
+        
+        col1, col2 = st.columns(2)
+        
+        with col1:
+            st.write("### Detailed Information")
+            st.write("Model:", model)
+            st.write("Temperature:", temperature)
+            st.write("Output Size:", output_size)
+            st.write("Bullet Points:")
+            st.write(bullet_points)
+            st.write("Humanize Text:", humanize_text)
+            st.write("Display Final Answer:", display_final_answer)
+            st.write("System Prompt:", system_prompt)
+            st.write("User Query:", user_query)
+            if display_final_answer:
+                st.write("### Original Response")
+                st.text_area("Original Response", value=response, height=600)
+        
+        with col2:
+            if display_final_answer:
+                processed_response = process_response(response, output_size, bullet_points, humanize_text, reduce_words)
+                additional_text = "Please review the response carefully before proceeding."
+                st.write("### Processed Response with Review")
+                st.text_area(response, value=processed_response + "\n" + additional_text, height=200)
+            else:
+                st.write("### Output Response")
+                st.text(response)
+        
+def query_groq(model, temperature, system_prompt, user_query, output_size, humanize_text, reduce_words):
+    try:
+        completion = client.chat.completions.create(
+            model=SUPPORTED_MODELS[model],
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_query}
+            ],
+            temperature=temperature,
+            max_tokens=MAX_TOKENS
+        )
+        if not completion.choices:
+            return "Error: No choices in the completion response."
+        return completion.choices[0].message.content
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+def process_response(text, output_size, bullet_points, humanize_text, reduce_words):
+    if reduce_words:
+        # Reduce word count by 50%
+        words = text.split()
+        text = " ".join(words[:len(words)//2])
+    
+    if output_size == "1-3 word sentences":
+        text = reduce_to_sentences(text, 1, 3)
+    elif output_size == "2-5 word sentences":
+        text = reduce_to_sentences(text, 2, 5)
+    elif output_size == "3-7 word sentences":
+        text = reduce_to_sentences(text, 3, 7)
+    elif output_size == "5-9 word sentences":
+        text = reduce_to_sentences(text, 5, 9)
+    elif output_size == "6-11 word sentences":
+        text = reduce_to_sentences(text, 6, 11)
+    
+    if bullet_points:
+        text = reduce_to_bullet_points(text, 1, 11)
+    
+    if humanize_text:
+        text = humanize(text)
+    
+    return text
+
+def reduce_to_bullet_points(text, min_words, max_words):
+    sentences = text.split('.')
+    bullet_points = []
+    for sentence in sentences:
+        words = sentence.strip().split()
+        if min_words <= len(words) <= max_words:
+            bullet_points.append(f"- {' '.join(words)}")
+    return '\n'.join(bullet_points)
+
+def reduce_to_sentences(text, min_words, max_words):
+    sentences = text.split('.')
+    filtered_sentences = []
+    for sentence in sentences:
+        words = sentence.strip().split()
+        if min_words <= len(words) <= max_words:
+            filtered_sentences.append(sentence.strip())
+    return ' '.join(filtered_sentences)
+
+def humanize(text):
+    # This can be replaced with a more sophisticated humanization logic as needed
+    return text.replace(". ", ". Let's consider this further. ")
+
+st.sidebar.info("build by dw")
+
+if __name__ == "__main__":
+    main()