Updated AI Content Optimizer with improvements

app.py

@@ -4,123 +4,104 @@ import pandas as pd
 import textstat
 import os
 import asyncio
+from textblob import TextBlob
 
 # Initialize OpenAI client
-api_key = os.getenv("OPENAI_API_KEY")
+client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
-if not api_key:
-    st.error("API key is missing. Please set the OPENAI_API_KEY environment variable.")
-    st.stop()
-
-client = openai.OpenAI(api_key=api_key)
-
-# Function to fetch available OpenAI models (filtering for GPT models only)
+# Function to fetch available OpenAI models
 def get_models():
     try:
-        all_models = client.models.list()
-        text_models = [m.id for m in all_models.data if "gpt" in m.id]  # Filter only GPT models
-        return text_models or ["gpt-4"]  # Default to GPT-4 if list is empty
+        models = client.models.list()
+        return [model.id for model in models.data]
     except Exception as e:
         st.error(f"Error fetching models: {e}")
-        return ["gpt-4"]  # Fallback model
+        return []
 
-# Function to generate AI response
-def generate_response(prompt, model, tone):
-    if model not in ["gpt-4", "gpt-3.5-turbo"]:
-        model = "gpt-4"  # Fallback to GPT-4 for unsupported models
+# Function to analyze text
+def analyze_text(text):
+    readability = textstat.flesch_reading_ease(text)
+    sentiment = TextBlob(text).sentiment.polarity
+    return readability, sentiment
 
-    try:
-        response = client.chat.completions.create(
-            model=model,
-            messages=[{"role": "system", "content": f"Rewrite this in {tone} style: {prompt}"}]
-        )
-        return response.choices[0].message.content.strip()
-    except Exception as e:
-        st.error(f"Error generating response: {e}")
-        return ""
+# Function to generate AI-enhanced content
+def generate_response(prompt, model, tone):
+    response = client.chat.completions.create(
+        model=model,
+        messages=[{"role": "system", "content": f"Rewrite this in {tone} style: {prompt}"}]
+    )
+    return response.choices[0].message.content.strip()
 
-# Function for batch processing with async calls
+# Function for batch processing asynchronously
 async def process_bulk(prompts, model, tone):
-    if model not in ["gpt-4", "gpt-3.5-turbo"]:
-        model = "gpt-4"  # Fallback to GPT-4 for unsupported models
-
     tasks = [
         client.chat.completions.acreate(
             model=model,
             messages=[{"role": "system", "content": f"Rewrite this in {tone} style: {p}"}]
         ) for p in prompts
     ]
-    try:
-        responses = await asyncio.gather(*tasks)
-        return [response.choices[0].message.content.strip() for response in responses]
-    except Exception as e:
-        st.error(f"Error processing bulk prompts: {e}")
-        return [""] * len(prompts)
+    responses = await asyncio.gather(*tasks)
+    return [response.choices[0].message.content.strip() for response in responses]
 
 # UI Structure
-st.title("AI Prompt Optimizer")
-st.write("Enhance, analyze, and optimize your prompts with AI!")
+st.title("🚀 AI Content Optimizer")
+st.write("Enhance, analyze, and optimize your content with AI!")
 
 # Select AI Provider
-provider = st.selectbox("Choose AI Provider", ["OpenAI", "Anthropic", "Cohere"])
+provider = st.selectbox("Choose AI Provider", ["OpenAI"])
 
-# Fetch available models dynamically
-if provider == "OpenAI":
-    available_models = get_models()
-    model_choice = st.selectbox("Choose AI Model", available_models)
+# Fetch available models
+display_models = get_models()
+if display_models:
+    model_choice = st.selectbox("Choose AI Model", display_models)
 else:
-    model_choice = st.text_input("Enter model name for selected provider")
-
-# Card-like structure for input
-st.markdown("### **Prompt Customization**")
-with st.expander("🔹 Customize Your Prompt"):
-    example_prompts = {
-        "Marketing": "Write an email subject line for a new product launch.",
-        "Storytelling": "Describe a futuristic city in a cyberpunk world.",
-        "Technical": "Explain quantum computing in simple terms."
-    }
-    category = st.selectbox("Choose a Use Case", list(example_prompts.keys()))
-    user_prompt = st.text_area("Enter your prompt:", value=example_prompts[category])
-    custom_tone = st.text_input("Enter a custom tone/style (e.g., poetic, technical, humorous)")
-    tone_choice = custom_tone if custom_tone else "Professional"
-
-# Evaluate readability before processing
-readability_score = textstat.flesch_reading_ease(user_prompt)
-st.write(f"**Original Readability Score:** {readability_score:.2f}")
-
-# Generate AI-enhanced prompt
-if st.button("🔄 Optimize Prompt"):
-    styled_prompt = generate_response(user_prompt, model_choice, tone_choice)
-    ai_readability_score = textstat.flesch_reading_ease(styled_prompt)
-    st.write(f"**Optimized Readability Score:** {ai_readability_score:.2f}")
-    st.text_area("Optimized Prompt:", styled_prompt, height=150)
-
-    # Store history in session state
+    model_choice = "gpt-3.5-turbo"
+
+# Prompt Customization
+st.markdown("### **Content Customization**")
+user_prompt = st.text_area("Enter your content:")
+tone_choice = st.selectbox("Choose a Writing Tone", ["Formal", "Casual", "Technical", "Poetic", "Persuasive"])
+
+if user_prompt:
+    readability, sentiment = analyze_text(user_prompt)
+    st.write(f"**Original Readability Score:** {readability:.2f}")
+    st.write(f"**Sentiment Score:** {sentiment:.2f} (Positive: 1, Negative: -1)")
+
+# Generate AI-enhanced content
+if st.button("🔄 Optimize Content"):
+    optimized_content = generate_response(user_prompt, model_choice, tone_choice)
+    optimized_readability, optimized_sentiment = analyze_text(optimized_content)
+    
+    st.write("### ✨ Optimized Content")
+    st.text_area("Optimized Content:", optimized_content, height=150)
+    
+    st.write(f"**Optimized Readability Score:** {optimized_readability:.2f}")
+    st.write(f"**Optimized Sentiment Score:** {optimized_sentiment:.2f}")
+    
     if "history" not in st.session_state:
         st.session_state["history"] = []
-    st.session_state["history"].append({"Original": user_prompt, "Optimized": styled_prompt})
-
-# Upload CSV for batch processing
-st.markdown("### **Batch Processing (CSV Upload)**")
-with st.expander("📂 Upload CSV for Bulk Optimization"):
-    uploaded_file = st.file_uploader("Upload a CSV file with a column named 'Prompt'", type=["csv"])
-    if uploaded_file:
-        df = pd.read_csv(uploaded_file)
-        if "Prompt" in df.columns:
-            prompts = df["Prompt"].tolist()
-            optimized_prompts = asyncio.run(process_bulk(prompts, model_choice, tone_choice))
-            df["Optimized_Prompt"] = optimized_prompts
-            st.write(df)
-            st.download_button("Download Optimized CSV", df.to_csv(index=False).encode('utf-8'), "optimized_prompts.csv", "text/csv")
-        else:
-            st.error("CSV must contain a column named 'Prompt'")
-
-# Show prompt history
-st.markdown("### **History of Optimized Prompts**")
+    st.session_state["history"].append({"Original": user_prompt, "Optimized": optimized_content})
+
+# Batch Processing
+st.markdown("### 📂 Bulk Optimization (CSV Upload)")
+uploaded_file = st.file_uploader("Upload a CSV file with a column named 'Content'", type=["csv"])
+if uploaded_file:
+    df = pd.read_csv(uploaded_file)
+    if "Content" in df.columns:
+        prompts = df["Content"].tolist()
+        optimized_prompts = asyncio.run(process_bulk(prompts, model_choice, tone_choice))
+        df["Optimized_Content"] = optimized_prompts
+        st.write(df)
+        st.download_button("Download Optimized CSV", df.to_csv(index=False).encode('utf-8'), "optimized_content.csv", "text/csv")
+    else:
+        st.error("CSV must contain a column named 'Content'")
+
+# Show Optimization History
+st.markdown("### 🔹 Optimization History")
 if "history" in st.session_state and st.session_state["history"]:
     for entry in st.session_state["history"][::-1]:
         st.write(f"🔹 **Original:** {entry['Original']}")
         st.write(f"✨ **Optimized:** {entry['Optimized']}")
         st.markdown("---")
 
-st.success("🚀 AI Prompt Optimizer Ready!")
+st.success("🚀 AI Content Optimizer Ready!")

requirements.txt

@@ -3,3 +3,10 @@ openai
 pandas
 textstat
 asyncio
+numpy
+scikit-learn
+transformers
+nltk
+tqdm
+python-dotenv
+