Update app.py

app.py

@@ -1,34 +1,71 @@
-import streamlit as st
-import requests
-
-st.title("Company Sentiment Analyzer")
-
-company_name = st.text_input("Enter Company Name", "Tesla")
-model_provider = st.selectbox("Model Provider", options=["Ollama", "Groq"])
-
-if st.button("Fetch Sentiment Data"):
-    api_url = (
-        f"http://localhost:8000/home?"
-        f"company_name={company_name}&model_provider={model_provider}"
-    )
-
-    try:
-        response = requests.get(api_url)
-        response.raise_for_status()
-
-        data = response.json()
-
-        st.subheader("Company Name")
-        st.write(data.get("company_name"))
-
-        st.subheader("Final Report")
-        st.write(data.get("final_report"))
-
-        st.subheader("🔊 Audio Output")
-        audio_file = "output.mp3"
-        if audio_file:
-            st.audio(audio_file)
-
-    except requests.exceptions.RequestException as e:
-        st.error(f"Error fetching data: {e}")
+import streamlit as st
+import requests
+from utils import (
+    fetch_from_web,
+    analyze_sentiment,
+    generate_comparative_sentiment,
+    generate_final_report,
+    get_summaries_by_sentiment,
+    translate,
+    text_to_speech,
+)
+
+st.title("Company Sentiment Analyzer")
+
+company_name = st.text_input("Enter Company Name", "Tesla")
+model_provider = st.selectbox("Model Provider", options=["Ollama", "Groq"])
+
+if st.button("Fetch Sentiment Data"):
+    web_results = fetch_from_web(company_name)
+
+    if "sources" not in web_results:
+        return {"error": "No sources found."}
+
+    sentiment_output = [
+        analyze_sentiment(article, model_provider)
+        for article in web_results["sources"][:5]
+    ]
+
+    comparative_sentiment = generate_comparative_sentiment(sentiment_output)
+
+    positive_summary, negative_summary, neutral_summary = get_summaries_by_sentiment(
+        sentiment_output
+    )
+
+    final_report = generate_final_report(
+        positive_summary,
+        negative_summary,
+        neutral_summary,
+        comparative_sentiment,
+        model_provider,
+    )
+
+    hindi_translation = translate(final_report, model_provider)
+    audio_path = text_to_speech(hindi_translation)
+
+    output_dict = {
+        "company_name": company_name,
+        "articles": sentiment_output,
+        "comparative_sentiment": comparative_sentiment,
+        "final_report": final_report,
+        "hindi_translation": hindi_translation,
+        "audio_url": audio_path,
+    }
+
+        st.subheader("Company Name")
+        st.write(output_dict.get("company_name"))
+
+        st.subheader("Final Report")
+        st.write(output_dict.get("final_report"))
+
+        st.subheader("🔊 Audio Output")
+        audio_file = "output.mp3"
+        if audio_file:
+            st.audio(audio_file)
+
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching data: {e}")
+
+
+        
 #